James A. Landay - IEEE Xplore Author Profile

Scheduled Maintenance on Monday 1/13/2025

Single article sales and account management will be unavailable from 5:00 AM - 7:00 PM ET (09:00 - 23:00 UTC). We apologize for the inconvenience.

IEEE.org
IEEE Xplore
IEEE SA
IEEE Spectrum
More Sites

- Donate
- Personal Sign In

Access provided by:

MIT Libraries

Access provided by:

MIT Libraries

ADVANCED SEARCH

Author details

James A. Landay

Also published under: J. A. Landay, J. Landay

Publications

22

Citations

1,079

Publications by Year

19932019

Co-Authors:

J. A. BilmesGaetano BorrielloJessica R. CauchardH. ChizeckTanzeem Choudhury

Show All Co-Authors (52)

James A. Landay

Also published under: J. A. Landay, J. Landay

Affiliation

Stanford University

Publication Topics

Flight Path,
Graphical User Interface,
Human Drone Interaction,
Interactive,
User Study,
Additional Hardware,
Dialogue System,
Discourse Markers,
Dwell Time,
Emotional Encoding,
Emotional Expressions,
Emotional Intensity

Biography

James A. Landay is a Professor of computer science and the Anand Rajaraman and Venky Harinarayan Professor with the School of Engineering, Stanford University. He specializes in human–computer interaction. He is the Founder and Co-Director of the World Lab, a joint research and educational effort with Tsinghua University in Beijing. He received the Ph.D. degree from Carnegie Mellon University. Contact him at landay@stanford.edu.(Based on document published on 13 August 2019).

Publications

22

Citations

1,079

Publications by Year

19932019

Co-Authors:

J. A. Bilmes
Gaetano Borriello
Jessica R. Cauchard
H. Chizeck
Tanzeem Choudhury

Show All Co-Authors (52)

Author's Published Works

Search History

Showing 1-25 of 37 results

Conferences (29)

Journals (8)

Sort

Filter Results

Show

Subscribed Content

Open Access Only

Range
Single Year
Yiu-ming Cheung(18)
Yiu-Ming Cheung(15)
Lei Xu(13)
De-Shuang Huang(5)
H.Z.H. Lai(4)
Department of Computer Science, Hong Kong Baptist University, Hong Kong, China(17)
Department of Computer Science and Engineering, Chinese University of Hong Kong, Hong Kong, China(9)
Faculty of Business Administration, Chinese University of Hong Kong, Hong Kong, China(3)
Center for Multimedia Signal Processing, Hong Kong Polytechnic University, Hong Kong, China(2)
Department of Mathematics, Zhongshan University, Guangzhou, China(2)
IEEE Transactions on Knowledge and Data Engineering(3)
2016 23rd International Conference on Pattern Recognition (ICPR)(2)
IEEE/IAFE 1996 Conference on Computational Intelligence for Financial Engineering (CIFEr)(2)
IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339)(2)
Proceedings of 2004 International Symposium on Intelligent Multimedia, Video and Speech Processing, 2004.(2)
IEEE(37)
Washington, DC, USA(4)
New York, NY, USA(3)
Hong Kong, China(2)
Portland, OR, USA(2)
Suzhou, China(2)
Competitive Learning(9)
Expectation Maximization(9)
Independent Component Analysis(8)
Learning Rate(7)
Exchange Rate(6)

Select All on Page

Sort By

Results

Simultaneous Dual-Views Reconstruction with Adaptive Dictionary and Low-Rank Representation

Shuangyan Yi;Zhenyu He;Yi Li;Yiu-Ming Cheung;Wen-Sheng Chen

2016 23rd International Conference on Pattern Recognition (ICPR)

Year: 2016 | Conference Paper |

HTML

Low-Rank Representation (LRR) is an effective self-expressiveness method, which uses the observed data itself as the dictionary to reconstruct the original data. LRR focuses on representing the global low-dimensional information, but ignores the real fact that data often resides on low-dimensional manifolds embedded in a high-dimensional data. Therefore, LRR can not capture the non-linear geometri...Show More

Simultaneous Dual-Views Reconstruction with Adaptive Dictionary and Low-Rank Representation

Shuangyan Yi;Zhenyu He;Yi Li;Yiu-Ming Cheung;Wen-Sheng Chen

2016 23rd International Conference on Pattern Recognition (ICPR)

Year: 2016 | Conference Paper |

Single image super-resolution with non-local balanced low-rank matrix restoration

Xinge You;Weiyong Xue;Jiajia Lei;Peng Zhang;Yiu-ming Cheung;Yuanyan Tang;Naiding Zhou

2016 23rd International Conference on Pattern Recognition (ICPR)

Year: 2016 | Conference Paper |

Cited by: Papers (1)

HTML

Single image super-resolution (SR) has gained popularity to construct a high-resolution (HR) image from a single low-resolution (LR) version. More recently, non-local self similarity (NSS) has been attracted enormous interests in the field of SR, and the non-local means (NLM)-based methods are classical NSS-based SR methods. However, NLM-based methods neglect the structure information in the patch...Show More

Single image super-resolution with non-local balanced low-rank matrix restoration

Xinge You;Weiyong Xue;Jiajia Lei;Peng Zhang;Yiu-ming Cheung;Yuanyan Tang;Naiding Zhou

2016 23rd International Conference on Pattern Recognition (ICPR)

Year: 2016 | Conference Paper |

Palmprint identification based on non-separable wavelet filter banks

Jie Wu;Xinge You;Yuan Yan Tang;Yiu-ming Cheung

2008 19th International Conference on Pattern Recognition

Year: 2008 | Conference Paper |

Cited by: Papers (1)

HTML

Creases, as a special salient feature of palmprint, are large in number and distributed at all directions. It changes slowly in a personpsilas whole life, which qualifies themselves as features in palmprint identification. In this paper, we devised a new algorithm of crease extraction by using non-separable bivariate wavelet filter banks with linear phase. Compared with the traditional wavelet, ou...Show More

Palmprint identification based on non-separable wavelet filter banks

Jie Wu;Xinge You;Yuan Yan Tang;Yiu-ming Cheung

2008 19th International Conference on Pattern Recognition

Year: 2008 | Conference Paper |

On Weight Design of Maximum Weighted Likelihood and an Extended EM Algorithm

Zhenyue Zhang;Yiu-ming Cheung

IEEE Transactions on Knowledge and Data Engineering

Year: 2006 | Volume: 18, Issue: 10 | Journal Article |

Cited by: Papers (7)

HTML

The recent Maximum Weighted Likelihood (MWL) [18], [19] has provided a general learning paradigm for density-mixture model selection and learning, in which weight design, however, is a key issue. This paper will therefore explore such a design, and through which a heuristic extended Expectation-Maximization (X-EM) algorithm is presented accordingly. Unlike the EM algorithm [1], the X-EM algorithm ...Show More

On Weight Design of Maximum Weighted Likelihood and an Extended EM Algorithm

Zhenyue Zhang;Yiu-ming Cheung

IEEE Transactions on Knowledge and Data Engineering

Year: 2006 | Volume: 18, Issue: 10 | Journal Article |

Improvements in Au wire bondability of rigid and flexible substrates using plasma cleaning

Yu Hin Chan;Jang-Kyo Kim;Deming Liu;Peter Chou Kee Liu;Yiu Ming Cheung;Ming Wai Ng

2005 6th International Conference on Electronic Packaging Technology

Year: 2005 | Conference Paper |

Cited by: Papers (3)

HTML

Thermosonic Au wire bonding is the most widely used interconnection technology in the BGA packages. Compare with Au ball bonding, the lower wire loop and finer bond pitch of Au wedge bond makes its ideal for low profile and high I/O applications, which is the subject of the current study. As bond pad surface cleanliness, substrate materials and wire bonding process parameters play a critical role ...Show More

Improvements in Au wire bondability of rigid and flexible substrates using plasma cleaning

Yu Hin Chan;Jang-Kyo Kim;Deming Liu;Peter Chou Kee Liu;Yiu Ming Cheung;Ming Wai Ng

2005 6th International Conference on Electronic Packaging Technology

Year: 2005 | Conference Paper |

Effect of plasma treatment of Au-Ni-Cu bond pads on process windows of Au wire bonding

Yu Hin Chan;Jang-Kyo Kim;Deming Liu;P.C.K. Liu;Yiu-Ming Cheung;Ming Wai Ng

IEEE Transactions on Advanced Packaging

Year: 2005 | Volume: 28, Issue: 4 | Journal Article |

Cited by: Papers (22) | Patents (1)

HTML

The wire bondability of Au-Ni-Cu bond pads with different Au plating schemes, including electrolytic and immersion plates, are evaluated after plasma treatment. The plasma cleaning conditions, such as cleaning power and time, are optimized based on the process window and wire pull strength measurements for different bond pad temperatures. Difference in the efficiency of plasma treatment in improvi...Show More

Effect of plasma treatment of Au-Ni-Cu bond pads on process windows of Au wire bonding

Yu Hin Chan;Jang-Kyo Kim;Deming Liu;P.C.K. Liu;Yiu-Ming Cheung;Ming Wai Ng

IEEE Transactions on Advanced Packaging

Year: 2005 | Volume: 28, Issue: 4 | Journal Article |

A reversible data hiding approach to mesh authentication

Hao-Tian Wu;Yiu-ming Cheung

The 2005 IEEE/WIC/ACM International Conference on Web Intelligence (WI'05)

Year: 2005 | Conference Paper |

Cited by: Papers (12)

HTML

This paper proposes a reversible data hiding method to authenticate 3D meshes by modulating the distances from the mesh faces to the mesh centroid to embed a fragile watermark. It keeps the modulation information in the watermarked mesh so that the reversibility of the embedding process is achieved. Since the embedded watermark is sensitive to geometrical and topological processing, unauthorized m...Show More

A reversible data hiding approach to mesh authentication

Hao-Tian Wu;Yiu-ming Cheung

The 2005 IEEE/WIC/ACM International Conference on Web Intelligence (WI'05)

Year: 2005 | Conference Paper |

On rival penalization controlled competitive learning for clustering with automatic cluster number selection

Yiu-ming Cheung

IEEE Transactions on Knowledge and Data Engineering

Year: 2005 | Volume: 17, Issue: 11 | Journal Article |

Cited by: Papers (52)

HTML

The existing rival penalized competitive learning (RPCL) algorithm and its variants have provided an attractive way to perform data clustering without knowing the exact number of clusters. However, their performance is sensitive to the preselection of the rival delearning rate. In this paper, we further investigate the RPCL and present a mechanism to control the strength of rival penalization dyna...Show More

On rival penalization controlled competitive learning for clustering with automatic cluster number selection

Yiu-ming Cheung

IEEE Transactions on Knowledge and Data Engineering

Year: 2005 | Volume: 17, Issue: 11 | Journal Article |

A new blind separation approach to ill-condition mixed sources

Hailin Liu;Yiu-ming Cheung

Proceedings of 2005 IEEE International Workshop on VLSI Design and Video Technology, 2005.

Year: 2005 | Conference Paper |

HTML

In this paper, we discuss solution property for blind separation of ill-condition mixed sources and therefore derive a sufficient and necessary condition. Further, we present a new contrast function, through which a new algorithm is proposed to directly separate the sources without time-consuming iterative steps. Under a loose condition, It is proved that any source signals theoretically satisfyin...Show More

A new blind separation approach to ill-condition mixed sources

Hailin Liu;Yiu-ming Cheung

Proceedings of 2005 IEEE International Workshop on VLSI Design and Video Technology, 2005.

Year: 2005 | Conference Paper |

Microarray data analysis using rival penalized EM algorithm in normal mixture models

Xing-Ming Zhao;Yiu-ming Cheung;De-Shuang Huang

Proceedings of 2005 IEEE International Workshop on VLSI Design and Video Technology, 2005.

Year: 2005 | Conference Paper |

Cited by: Papers (1)

HTML

Microarray technology is a useful tool for monitoring the expressed levels of thousands of genes simultaneously. Recently, mixture modelling has been used to extract information from expressed genes. It utilizes two separate steps to estimate the number of classes and model parameters, respectively, which however may be time-consuming and fall into sub-optimal solutions. In this paper, we therefor...Show More

Microarray data analysis using rival penalized EM algorithm in normal mixture models

Xing-Ming Zhao;Yiu-ming Cheung;De-Shuang Huang

Proceedings of 2005 IEEE International Workshop on VLSI Design and Video Technology, 2005.

Year: 2005 | Conference Paper |

Maximum weighted likelihood via rival penalized EM for density mixture clustering with automatic model selection

Yiu-ming Cheung

IEEE Transactions on Knowledge and Data Engineering

Year: 2005 | Volume: 17, Issue: 6 | Journal Article |

Cited by: Papers (73)

HTML

Expectation-maximization (EM) algorithm (A.P. Dempster et al., 1977) has been extensively used in density mixture clustering problems, but it is unable to-perform model selection automatically. This paper, therefore, proposes to learn the model parameters via maximizing a weighted likelihood. Under a specific weight design, we give out a rival penalized expectation-maximization (RPEM) algorithm, w...Show More

Maximum weighted likelihood via rival penalized EM for density mixture clustering with automatic model selection

Yiu-ming Cheung

IEEE Transactions on Knowledge and Data Engineering

Year: 2005 | Volume: 17, Issue: 6 | Journal Article |

Using FCMC, FVS, and PCA techniques for feature extraction of multispectral images

Zhan-Li Sun;De-Shuang Huang;Yiu-Ming Cheung;Jiming Liu;Guang-Bin Huang

IEEE Geoscience and Remote Sensing Letters

Year: 2005 | Volume: 2, Issue: 2 | Journal Article |

Cited by: Papers (57) | Patents (3)

HTML

In this letter, a new nonlinear approach based on a combination of the fuzzy c-means clustering (FCMC), feature vector selection and principal component analysis (PCA) is proposed to extract features of multispectral images when a very large number of samples need to be processed. The main contribution of this letter is to provide a preprocessing method for classifying these images with higher acc...Show More

Using FCMC, FVS, and PCA techniques for feature extraction of multispectral images

Zhan-Li Sun;De-Shuang Huang;Yiu-Ming Cheung;Jiming Liu;Guang-Bin Huang

IEEE Geoscience and Remote Sensing Letters

Year: 2005 | Volume: 2, Issue: 2 | Journal Article |

Bark texture feature extraction based on statistical texture analysis

Yuan-Yuan Wan;Ji-Xiang Du;De-Shuang Huang;Zheru Chi;Yiu-Ming Cheung;Xiao-Feng Wang;Guo-Jun Zhang

Proceedings of 2004 International Symposium on Intelligent Multimedia, Video and Speech Processing, 2004.

Year: 2004 | Conference Paper |

Cited by: Papers (14)

HTML

This paper quantitatively describes and discusses the usefulness of texture analysis methods for the recognition of bark. Comparative studies of bark texture feature extraction are performed for the four texture analysis methods such as the gray level run-length method (RLM), co-occurrence matrices method (COMM) and histogram method (HM) as well as auto-correlation method (ACM). Specifically, we u...Show More

Bark texture feature extraction based on statistical texture analysis

Yuan-Yuan Wan;Ji-Xiang Du;De-Shuang Huang;Zheru Chi;Yiu-Ming Cheung;Xiao-Feng Wang;Guo-Jun Zhang

Proceedings of 2004 International Symposium on Intelligent Multimedia, Video and Speech Processing, 2004.

Year: 2004 | Conference Paper |

A hypersphere method for plant leaves classification

Guo-Jun Zhang;Xiao-Feng Wang;De-Shuang Huang;Zheru Chi;Yiu-Ming Cheung;Ji-Xiang Du;Yuan-Yuan Wan

Proceedings of 2004 International Symposium on Intelligent Multimedia, Video and Speech Processing, 2004.

Year: 2004 | Conference Paper |

Cited by: Papers (10)

HTML

The recognition of the tens of thousands of kinds of plants on Earth is an important and difficult task. If we use a nearest neighbour classifier to solve it, millions of training data are needed to obtain a high correct recognition rate. However, the corresponding classification process is quite time-consuming. We propose a new approach using a moving median centers hypersphere technique to recog...Show More

A hypersphere method for plant leaves classification

Guo-Jun Zhang;Xiao-Feng Wang;De-Shuang Huang;Zheru Chi;Yiu-Ming Cheung;Ji-Xiang Du;Yuan-Yuan Wan

Proceedings of 2004 International Symposium on Intelligent Multimedia, Video and Speech Processing, 2004.

Year: 2004 | Conference Paper |

A dynamically switched crossover for genetic algorithms

Liang Ming;Yiu-Ming Cheung;Yu-Ping Wang

Proceedings of 2004 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.04EX826)

Year: 2004 | Conference Paper |

HTML

The traditional crossover operator performs the constant crossover between two parents without considering their homogeneity. Actually, the more homogeneous the parents are, the more disruptive the crossover should be. In this paper, a self-adaptive mechanism named adaptive recombination with three sub-populations (ARTS) is therefore presented to control the crossover operator of a genetic algorit...Show More

A dynamically switched crossover for genetic algorithms

Liang Ming;Yiu-Ming Cheung;Yu-Ping Wang

Proceedings of 2004 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.04EX826)

Year: 2004 | Volume: 5 | Conference Paper |

A novel mixed pixels unmixing method for multispectral images

Jun-Hua Han;De-Shuang Huang;Zhan-Li Sun;Yiu-ming Cheung

2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541)

Year: 2004 | Conference Paper |

HTML

The key to mixed pixels unmixing is to determine a set of spectral endmembers that are representative of the surface components in the area covered by multispectral images. We propose a geometric method utilizing the spectrum of two or three bands to determine the endmembers under the assumption that the endmembers are the pure surface components that lie in the extremities of the scatter plot of ...Show More

A novel mixed pixels unmixing method for multispectral images

Jun-Hua Han;De-Shuang Huang;Zhan-Li Sun;Yiu-ming Cheung

2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541)

Year: 2004 | Volume: 4 | Conference Paper |

A rival penalized EM algorithm towards maximizing weighted likelihood for density mixture clustering with automatic model selection

Yiu-ming Cheung

Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004.

Year: 2004 | Conference Paper |

Cited by: Papers (3)

HTML

How to determine the number of clusters is the intractable problem in clustering analysis. We propose a new learning paradigm named maximum weighted likelihood (MwL), in which the weights can be designed. Accordingly, we develop a novel rival penalized expectation-maximization (RPEM) algorithm, whose intrinsic rival penalization mechanism enables the redundant densities in the mixture to be gradua...Show More

A rival penalized EM algorithm towards maximizing weighted likelihood for density mixture clustering with automatic model selection

Yiu-ming Cheung

Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004.

Year: 2004 | Volume: 4 | Conference Paper |

A parallel architecture using discrete wavelet transform for fast ICA implementation

Rong-Bo Huang;Yiu-Ming Cheung;Shi-Ming Zhu

International Conference on Neural Networks and Signal Processing, 2003. Proceedings of the 2003

Year: 2003 | Conference Paper |

Cited by: Papers (1)

HTML

This paper utilizes a discrete wavelet transform to present a parallel architecture for independent component analysis (ICA), which is a hybrid system consisting of two sub-ICA processes. One process takes the high-frequency wavelet part of observations as its input, meanwhile the other process takes the low-frequency part. Their results are then merged to generate the final ICA results. Compared ...Show More

A parallel architecture using discrete wavelet transform for fast ICA implementation

Rong-Bo Huang;Yiu-Ming Cheung;Shi-Ming Zhu

International Conference on Neural Networks and Signal Processing, 2003. Proceedings of the 2003

Year: 2003 | Volume: 2 | Conference Paper |

A divide-and-conquer based radial basis function network with application to recurrent modelling

Rong-bo Huang;Yiu-ming Cheung;Lap-tak Law

Proceedings of the International Joint Conference on Neural Networks, 2003.

Year: 2003 | Conference Paper |

Cited by: Papers (1)

HTML

In this paper, a new architecture of divide-and-conquer based radial basis function network (DCRBF) and its learning algorithm are presented. The DCRBF network is a hybrid system consisting of several sub-RBF networks, each of which individually takes a sub-input space at its input. The output of this new architecture is linear combination of the sub-network's outputs with the coefficients tuned t...Show More

A divide-and-conquer based radial basis function network with application to recurrent modelling

Rong-bo Huang;Yiu-ming Cheung;Lap-tak Law

Proceedings of the International Joint Conference on Neural Networks, 2003.

Year: 2003 | Volume: 1 | Conference Paper |

Color image segmentation using rival penalized controlled competitive learning

Lap-tak Law;Yiu-ming Cheung

Proceedings of the International Joint Conference on Neural Networks, 2003.

Year: 2003 | Conference Paper |

Cited by: Papers (13)

HTML

Color image segmentation has been extensively applied to a lot of applications such as pattern recognition, image compression and matching. In the literature, conventional k-means is one common algorithm used in pixel-based image segmentation. However, it needs to pre-assign an appropriate cluster number before performing clustering, which is an intractable problem from a practical viewpoint. In c...Show More

Color image segmentation using rival penalized controlled competitive learning

Lap-tak Law;Yiu-ming Cheung

Proceedings of the International Joint Conference on Neural Networks, 2003.

Year: 2003 | Volume: 1 | Conference Paper |

Dual multivariate auto-regressive modeling in state space for temporal signal separation

Yiu-ming Cheung;Lei Xu

IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics)

Year: 2003 | Volume: 33, Issue: 3 | Journal Article |

Cited by: Papers (25)

HTML

Many existing independent component analysis (ICA) approaches result in deteriorated performance in temporal source separation because they have not taken into consideration of the underlying temporal structure of sources. In this paper, we model temporal sources as a general multivariate auto-regressive (AR) process whereby an underlying multivariate AR process in observation space is obtained. I...Show More

Dual multivariate auto-regressive modeling in state space for temporal signal separation

Yiu-ming Cheung;Lei Xu

IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics)

Year: 2003 | Volume: 33, Issue: 3 | Journal Article |

An extended ASLD trading system to enhance portfolio management

Kei-keung Hung;Yiu-ming Cheung;Lei Xu

IEEE Transactions on Neural Networks

Year: 2003 | Volume: 14, Issue: 2 | Journal Article |

Cited by: Papers (12)

HTML

An adaptive supervised learning decision (ASLD) trading system has been presented by Xu and Cheung (1997) to optimize the expected returns of investment without considering risks. In this paper, we propose an extension of the ASLD system (EASLD), which combines the ASLD with a portfolio optimization scheme to take a balance between the expected returns and risks. This new system not only keeps the...Show More

An extended ASLD trading system to enhance portfolio management

Kei-keung Hung;Yiu-ming Cheung;Lei Xu

IEEE Transactions on Neural Networks

Year: 2003 | Volume: 14, Issue: 2 | Journal Article |

Wire bondability of Au/Ni bond pads: effects of metallisation schemes and processing conditions

Yu Hin Chan;Jang-kyo Kim;D. Liu;C.K. Liu;Yiu Ming Cheung;Ming Wai

Proceedings of the 4th International Symposium on Electronic Materials and Packaging, 2002.

Year: 2002 | Conference Paper |

HTML

The wire bondability and surface characteristics were studied of Au/Ni bond pads on organic FR-4 PCBs with different gold plating schemes and gold thickness. Three different plating techniques were used to deposit gold layers, namely the electrolytic gold plating, immersion gold plating and immersion gold plating followed by electrolytic gold plating. Process windows for the individual metallisati...Show More

Wire bondability of Au/Ni bond pads: effects of metallisation schemes and processing conditions

Yu Hin Chan;Jang-kyo Kim;D. Liu;C.K. Liu;Yiu Ming Cheung;Ming Wai

Proceedings of the 4th International Symposium on Electronic Materials and Packaging, 2002.

Year: 2002 | Conference Paper |

An experimental study: on reducing RBF input dimension by ICA and PCA

Rong-Bo Huang;Lap-Tak Law;Yiu-Ming Cheung

Proceedings. International Conference on Machine Learning and Cybernetics

Year: 2002 | Conference Paper |

Cited by: Papers (9)

HTML

Experimentally investigates using independent component analysis (ICA) and principle component analysis (PCA) in the reduction of the input dimension of a radial basis function (RBF) network such that the net's complexity is reduced. The results have shown that a RBF network with ICA as an input pre-process has similar generalization ability to the one without pre-processing, but the former's perf...Show More

An experimental study: on reducing RBF input dimension by ICA and PCA

Rong-Bo Huang;Lap-Tak Law;Yiu-Ming Cheung

Proceedings. International Conference on Machine Learning and Cybernetics

Year: 2002 | Volume: 4 | Conference Paper |

Temporal principal component analysis - advances in dual auto-regressive modeling for blind Gaussian process identification

Yiu-ming Cheung

IEEE International Conference on Systems, Man and Cybernetics

Year: 2002 | Conference Paper |

Cited by: Papers (1)

HTML

A recent paper (Cheung, 2001) has studied the blind identification of Gaussian source process through a general temporal independent component analysis (ICA) approach named dual autoregressive modelling. It is actually a temporal extension of the classical principal component analysis without considering the principal order of the components. In this paper, we further show the identifiable conditi...Show More

Temporal principal component analysis - advances in dual auto-regressive modeling for blind Gaussian process identification

Yiu-ming Cheung

IEEE International Conference on Systems, Man and Cybernetics

Year: 2002 | Volume: 3 | Conference Paper |

IEEE Personal Account

Change username/password

Purchase Details

Payment Options
View Purchased Documents

Profile Information

Communications Preferences
Profession and Education
Technical interests

Need Help?

US & Canada: +1 800 678 4333
Worldwide: +1 732 981 0060
Contact & Support

Follow

About IEEE Xplore | Contact Us | Help | Accessibility | Terms of Use | Nondiscrimination Policy | IEEE Ethics Reporting | Sitemap | IEEE Privacy Policy

A public charity, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.

© Copyright 2025 IEEE - All rights reserved, including rights for text and data mining and training of artificial intelligence and similar technologies.

IEEE Account

Change Username/Password
Update Address

Purchase Details

Payment Options
Order History
View Purchased Documents

Profile Information

Communications Preferences
Profession and Education
Technical Interests

Need Help?

US & Canada: +1 800 678 4333
Worldwide: +1 732 981 0060
Contact & Support

About IEEE Xplore
Contact Us
Help
Accessibility
Terms of Use
Nondiscrimination Policy
Sitemap
Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.
© Copyright 2025 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.

Test Whats new message.