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De-Shuang Huang - IEEE Xplore Author Profile

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Author details

De-Shuang Huang

Also published under: De-shuang Huang

Publications

74

Citations

626

Publications by Year

19982016

Co-Authors:

Bing WangBing-Yu SunBo LiCan-Yi LuChao Wang

Show All Co-Authors (66)

De-Shuang Huang

Also published under: De-shuang Huang

Affiliation

College of Electronics and Information Engineering

Tongji University

Shanghai, China

Publication Topics

Artificial Neural Network,
Artificial Neural Network Model,
Binding Profiles,
Biological Neural Networks,
ChIP-seq Data,
ChIP-seq Datasets,
Chromatin Immunoprecipitation,
Dynamic Network,
Euclidean Space,
Experimental Evaluation,
Genomic Position,
High Adaptability

Biography

De-Shuang Huang (SM'98) received the B.Sc. degree in electronic engineering from the Institute of Electronic Engineering, Hefei, China, in 1986, the M.Sc. degree in electronic engineering from the National Defense University of Science and Technology, Changsha, China, in 1989, and the Ph.D. degree in electronic engineering from Xidian University, Xian, China, in 1993.,From 1993 to 1997, he was a Postdoctoral Student at the Beijing Institute of Technology, Beijing, China, and the National Key Laboratory of Pattern Recognition, Chinese Academy of Sciences (CAS), Beijing. In 2000, he joined the Institute of Intelligent Machines, CAS, as a member of the Hundred Talents Program of CAS. From 2000 to 2001, he worked as Research Associate at the Ho...

Publications

74

Citations

626

Publications by Year

19982016

Co-Authors:

Bing Wang
Bing-Yu Sun
Bo Li
Can-Yi Lu
Chao Wang

Show All Co-Authors (66)

Author's Published Works

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Showing 1-25 of 74 results

Conferences (66)

Journals (7)

Magazines (1)

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De-Shuang Huang(72)
Zheru Chi(7)
Wenbo Zhao(7)
Ji-Xiang Du(5)
Xiao-Feng Wang(4)
Department of Automation, University of Science and Technology, Hefei, Anhui, China(16)
Institute of Intelligent Machines, Chinese Academy and Sciences, Hefei, Anhui, China(13)
Department of Automation, University of Science and Technology, China(6)
Center for Multimedia Signal Processing, Hong Kong Polytechnic University, Hong Kong, China(6)
Intelligent Computing Lab, Institute of Intelligent Machines, Chinese Academy and Sciences, Hefei, Anhui, China(6)
2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541)(6)
Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005.(6)
2007 IEEE Congress on Evolutionary Computation(5)
Proceedings of the International Joint Conference on Neural Networks, 2003.(5)
6th International Conference on Signal Processing, 2002.(4)
IEEE(74)
Beijing, China(9)
Washington, DC, USA(7)
Singapore(6)
Budapest, Hungary(5)
Hong Kong, China(5)
Neural Network(37)
Feed-forward Network(18)
Hidden Layer(13)
Probabilistic Neural Network(11)
Roots Of Polynomial(11)

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Results

Representing logical relations automatically by Probabilistic Logical Dynamical Neural Network

Gang Wang;De-Shuang Huang

2016 International Joint Conference on Neural Networks (IJCNN)

Year: 2016 | Conference Paper |

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Most of current ANN represents relations in the way of functional approximation. It is good for representing the numeric relations or ratios of things. However, it is not proper to represent logical relations in the form of ratio. Therefore, aiming for representing logical relations directly, we propose a new ANN model PLDNN (Probabilistic Logical Dynamical Neural Network). It defines new neurons ...Show More

Representing logical relations automatically by Probabilistic Logical Dynamical Neural Network

Gang Wang;De-Shuang Huang

2016 International Joint Conference on Neural Networks (IJCNN)

Year: 2016 | Conference Paper |

Imputation of ChIP-seq datasets via Low Rank Convex Co-Embedding

Lin Zhu;Wei-Li Guo;De-Shuang Huang;Can-Yi Lu

2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

Year: 2015 | Conference Paper |

Cited by: Papers (1)

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In recent years, thanks to the efforts of individual scientists and research consortiums, a huge amount of chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) experimental data have been accumulated. Although several recent studies have demonstrated that a wealth of insights can be gained by integrative analysis of these data, owing to cost, time or sample material avai...Show More

Imputation of ChIP-seq datasets via Low Rank Convex Co-Embedding

Lin Zhu;Wei-Li Guo;De-Shuang Huang;Can-Yi Lu

2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

Year: 2015 | Conference Paper |

A scalable rayleigh-ritz style method for large scale Canonical Correlation Analysis

Lin Zhu;De-Shuang Huang

The 2012 International Joint Conference on Neural Networks (IJCNN)

Year: 2012 | Conference Paper |

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In this paper, we propose a novel inverse-free iterative algorithm for efficiently solving the generalized eigenvalue problem in Canonical Correlation Analysis (CCA). Compared with the state-of-the-art approach of reformulating it as a regression problem, our method is more efficient and can find the exact solution to the original generalized eigenvalue problem under a milder condition. Numerical ...Show More

A scalable rayleigh-ritz style method for large scale Canonical Correlation Analysis

Lin Zhu;De-Shuang Huang

The 2012 International Joint Conference on Neural Networks (IJCNN)

Year: 2012 | Conference Paper |

Notice of Retraction: Maximum Generialized Fisher Criterion

Bo Li;De-Shuang Huang

2010 3rd International Conference on Computer Science and Information Technology

Year: 2010 | Conference Paper |

Cited by: Papers (1)

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Retracted.Show More

Notice of Retraction: Maximum Generialized Fisher Criterion

Bo Li;De-Shuang Huang

2010 3rd International Conference on Computer Science and Information Technology

Year: 2010 | Volume: 6 | Conference Paper |

Palmprint identification based on directional representation

Wei Jia;De-Shuang Huang;Dacheng Tao;David Zhang

2008 IEEE International Conference on Systems, Man and Cybernetics

Year: 2008 | Conference Paper |

Cited by: Papers (7)

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In this paper, we propose a novel approach for palmprint identification, which contains two interesting components. Firstly, we propose the directional representation for appearance based approaches. The new representation is robust to drastic illumination changes and preserves important discriminative information for classification. We then generate virtual samples to enlarge the training set to ...Show More

Palmprint identification based on directional representation

Wei Jia;De-Shuang Huang;Dacheng Tao;David Zhang

2008 IEEE International Conference on Systems, Man and Cybernetics

Year: 2008 | Conference Paper |

An improvement on learning with local and global consistency

Jie Gui;De-Shuang Huang;Zhuhong You

2008 19th International Conference on Pattern Recognition

Year: 2008 | Conference Paper |

Cited by: Papers (3)

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A modified version for semi-supervised learning algorithm with local and global consistency was proposed in this paper. The new method adds the label information, and adopts the geodesic distance rather than Euclidean distance as the measure of the difference between two data points when conducting calculation. In addition we add class prior knowledge. It was found that the effect of class prior k...Show More

An improvement on learning with local and global consistency

Jie Gui;De-Shuang Huang;Zhuhong You

2008 19th International Conference on Pattern Recognition

Year: 2008 | Conference Paper |

Improving the robustness of ISOMAP by de-noising

Bo Li;De-Shuang Huang;Chao Wang

2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence)

Year: 2008 | Conference Paper |

Cited by: Papers (2)

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ISOMAP is a manifold learning based algorithm for dimensionality reduction, which is successfully applied to data visualization. However, there exists such limitation in classical ISOMAP that the algorithm is sensitive to noises, especially outliers. So in this paper an extended ISOMAP algorithm is put forward to solve the problem of sensitivity. The proposed algorithm follows the method of classi...Show More

Improving the robustness of ISOMAP by de-noising

Bo Li;De-Shuang Huang;Chao Wang

2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence)

Year: 2008 | Conference Paper |

Constrained Maximum Variance Mapping

Bo Li;De-Shuang Huang;Kun-Hong Liu

2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence)

Year: 2008 | Conference Paper |

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In this paper, an efficient feature extraction method named as Constrained Maximum Variance Mapping (CMVM) is developed for dimensionality reduction. The proposed algorithm can be viewed as a linear approximation of multi-manifolds based learning approach, which takes the local geometry and manifold labels into account. After the local scatters have been characterized, the proposed method focuses ...Show More

Constrained Maximum Variance Mapping

Bo Li;De-Shuang Huang;Kun-Hong Liu

2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence)

Year: 2008 | Conference Paper |

Pattern classification with a PSO optimization based elliptical basis function neural networks

Ji-Xiang Du;De-shuang Huang;Zeng-Fu Wang

2007 IEEE Congress on Evolutionary Computation

Year: 2007 | Conference Paper |

Cited by: Papers (1)

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In this paper, a novel model of elliptical basis function neural networks (EBFNN) based on a hybrid optimization algorithm is proposed. Firstly, a geometry analytic algorithm is applied to construct the hyper-ellipsoid units of hidden layer of the EBFNN, i.e., an initial structure of the EBFNN, which is further pruned by the particle swarm optimization (PSO) algorithm. And the shape parameters of ...Show More

Pattern classification with a PSO optimization based elliptical basis function neural networks

Ji-Xiang Du;De-shuang Huang;Zeng-Fu Wang

2007 IEEE Congress on Evolutionary Computation

Year: 2007 | Conference Paper |

An evolutionary modular neural network for unbalanced pattern classifications

Zhong-Qiu Zhao;De-Shuang Huang

2007 IEEE Congress on Evolutionary Computation

Year: 2007 | Conference Paper |

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In this paper, an evolutionary modular neural network is proposed to solve multi-class problems with unbalanced training sets. The proposed model can transform an unbalanced classification problem into a set of symmetrical two-class problems, each of which can be solved by a single simple neural network. The experimental results show that the proposed method reduces time consumption for training a...Show More

An evolutionary modular neural network for unbalanced pattern classifications

Zhong-Qiu Zhao;De-Shuang Huang

2007 IEEE Congress on Evolutionary Computation

Year: 2007 | Conference Paper |

Multi-sub-swarm particle swarm optimization algorithm for multimodal function optimization

Jun Zhang;De-Shuang Huang;Kun-Hong Liu

2007 IEEE Congress on Evolutionary Computation

Year: 2007 | Conference Paper |

Cited by: Papers (12)

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This paper presents a novel multi-sub-swarm Particle Swarm Optimization (PSO) algorithm. The proposed algorithm can effectively imitate a natural ecosystem, in which the different sub-populations can compete with each other. After competing, the winner will continue to explore the original district, while the loser will be obliged to explore another district. Four benchmark multimodal functions of...Show More

Multi-sub-swarm particle swarm optimization algorithm for multimodal function optimization

Jun Zhang;De-Shuang Huang;Kun-Hong Liu

2007 IEEE Congress on Evolutionary Computation

Year: 2007 | Conference Paper |

Improving the performance of ICA based microarray data prediction models with genetic algorithm

Kun-Hong Liu;De-Shuang Huang;Bo Li

2007 IEEE Congress on Evolutionary Computation

Year: 2007 | Conference Paper |

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It is a challenging task to diagnose tumor type precisely based on microarray data because the number of variables p (genes) is far larger than that of samples, n. Many independent component analysis (ICA) based models had been proposed to tackle the microarray data classification problem with great success. Although it was pointed out that different independent components (ICs) are of different b...Show More

Improving the performance of ICA based microarray data prediction models with genetic algorithm

Kun-Hong Liu;De-Shuang Huang;Bo Li

2007 IEEE Congress on Evolutionary Computation

Year: 2007 | Conference Paper |

Microarray data prediction by evolutionary classifier ensemble system

Kun-Hong Liu;De-Shuang Huang;Jun Zhang

2007 IEEE Congress on Evolutionary Computation

Year: 2007 | Conference Paper |

Cited by: Papers (1)

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Microarray data prediction is a hard task due to the small sample and high dimension property. This paper proposes a classifier fusion approch to solve this problem based on genetic algorithm (GA). In this fusion strategy, GA is applied to select proper feature subsets and weight value for the fusion of classifiers. The experimental results show that the proposed scheme can improve the prediction ...Show More

Microarray data prediction by evolutionary classifier ensemble system

Kun-Hong Liu;De-Shuang Huang;Jun Zhang

2007 IEEE Congress on Evolutionary Computation

Year: 2007 | Conference Paper |

Predicting Protein-Protein Interaction Sites using Radial Basis Function Neural Networks

Bing Wang;Hau San Wong;Peng Chen;Hong-Qiang Wang;De-Shuang Huang

The 2006 IEEE International Joint Conference on Neural Network Proceedings

Year: 2006 | Conference Paper |

Cited by: Papers (4)

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Identifying protein-protein interaction sites is crucial for understanding of the principles of biological systems and processes, as well as mutant design. This paper describes a novel method that can predict protein interaction sites in heterocomplexes using information of evolutionary conservation and spatial sequence profile. A predictor was generated to distinguish the interface residues from ...Show More

Predicting Protein-Protein Interaction Sites using Radial Basis Function Neural Networks

Bing Wang;Hau San Wong;Peng Chen;Hong-Qiang Wang;De-Shuang Huang

The 2006 IEEE International Joint Conference on Neural Network Proceedings

Year: 2006 | Conference Paper |

Long-Range Interaction Analysis using Principal Component Analysis

Peng Chen;Bing Wang;Hau-San Wong;De-Shuang Huang

The 2006 IEEE International Joint Conference on Neural Network Proceedings

Year: 2006 | Conference Paper |

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This paper analyzes the long-range interactions, which plays a fundamental and important role in many biologic fields, between residues in protein using principal component analysis (PCA). Firstly, one angular coordinate system of long-range interaction regions is constructed conveniently. Afterwards, a matrix of the angular values of residues can be analyzed by principal component analysis techni...Show More

Long-Range Interaction Analysis using Principal Component Analysis

Peng Chen;Bing Wang;Hau-San Wong;De-Shuang Huang

The 2006 IEEE International Joint Conference on Neural Network Proceedings

Year: 2006 | Conference Paper |

Spectrum Analysis Based onWindows with Variable Widths for Online Signature Verification

Zhong-hua Quan;De-shuang Huang;Xiao-lei Xia;M.R. Lyu;Tat-Ming Lok

18th International Conference on Pattern Recognition (ICPR'06)

Year: 2006 | Conference Paper |

Cited by: Papers (3)

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In this paper, an online signature verification scheme based on spectrum analysis and Mahalanobis decision is proposed. We firstly divided signatures to a number of frames with variable widths according to the characteristics of the time sequences, and then employed the fast Fourier transformation (FFT) to extract the spectrum of signatures. The distance between the Fourier coefficient within the ...Show More

Spectrum Analysis Based onWindows with Variable Widths for Online Signature Verification

Zhong-hua Quan;De-shuang Huang;Xiao-lei Xia;M.R. Lyu;Tat-Ming Lok

18th International Conference on Pattern Recognition (ICPR'06)

Year: 2006 | Volume: 2 | Conference Paper |

A novel image retrieval system based on BP neural network

Jun-Hua Han;De-Shuang Huang;Tat-Ming Lok;M.R. Lyu

Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005.

Year: 2005 | Conference Paper |

Cited by: Papers (4)

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This paper presents a novel BP-based image retrieval (BPBIR) system, which is based on the observation that the images users need are often similar to a set of images with the same conception instead of one query image and the assumption that there is a nonlinear relationship between different features. If users aren't satisfied with the retrieved results, relevance feedback method is used to enha...Show More

A novel image retrieval system based on BP neural network

Jun-Hua Han;De-Shuang Huang;Tat-Ming Lok;M.R. Lyu

Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005.

Year: 2005 | Volume: 4 | Conference Paper |

Neural network-based shape recognition using generalized differential evolution training algorithm

Ji-Xiang Du;De-Shuang Huang;Xiao-Feng Wang;Xiao Gu

Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005.

Year: 2005 | Conference Paper |

Cited by: Papers (3)

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In this paper a new method for recognition of 2D occluded shapes based on neural network using generalized differential evolution training algorithm is proposed. Firstly, a generalized differential evolution (GDE) algorithm is introduced. And this GDE algorithm is applied to train multilayer perceptron neural networks. Then a new shape feature, refer to as multiscale Fourier descriptors (MFDs) is ...Show More

Neural network-based shape recognition using generalized differential evolution training algorithm

Ji-Xiang Du;De-Shuang Huang;Xiao-Feng Wang;Xiao Gu

Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005.

Year: 2005 | Volume: 4 | Conference Paper |

Blind inversion of Wiener system for single source using nonlinear blind source separation

Zhan Li-Sun;De-Shuang Huang;Chun-Hou Zheng;Li Shang

Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005.

Year: 2005 | Conference Paper |

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In this paper, a nonlinear blind source separation system with post-nonlinear mixing; model, and an unsupervised learning algorithm for the parameters of this separating system are presented for blind inversion of Wiener system for single source. The proposed method firstly changes the deconvolution part of Wiener system into a special case of linear blind source separation (BSS). Then the nonline...Show More

Blind inversion of Wiener system for single source using nonlinear blind source separation

Zhan Li-Sun;De-Shuang Huang;Chun-Hou Zheng;Li Shang

Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005.

Year: 2005 | Volume: 2 | Conference Paper |

Prediction of contact map integrated PNN with conformational energy

Peng Chen;De-Shuang Huang;Bing Wang;Yunping Zhu;Yixue Li

Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005.

Year: 2005 | Conference Paper |

Cited by: Papers (1)

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This paper presents a novel method to solve the protein's three-dimensional structure prediction problem. It is a machine learning approach by integrating probabilistic neural network (PNN) with conformational energy function (CEF) based on chemico-physical knowledge of amino acids. In this method, firstly, the principal components are extracted from selected protein structures with lower sequence...Show More

Prediction of contact map integrated PNN with conformational energy

Peng Chen;De-Shuang Huang;Bing Wang;Yunping Zhu;Yixue Li

Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005.

Year: 2005 | Volume: 1 | Conference Paper |

Characterizing human gene splice sites using evolved regular expressions

Jing-Jing Li;De-Shuang Huang;R.M. MacCallum;Xiao-Run Wu

Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005.

Year: 2005 | Conference Paper |

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

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In this paper, an algorithm using evolved regular expressions to characterize and predict human gene splice sites without any prior knowledge is described. In contrast to previous pattern-based approaches to the splice site detection problem, the patterns to be matched are unknown in advance and discovered using a supervised learning approach. We have used a genetic programming based system, PerlG...Show More

Characterizing human gene splice sites using evolved regular expressions

Jing-Jing Li;De-Shuang Huang;R.M. MacCallum;Xiao-Run Wu

Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005.

Year: 2005 | Volume: 1 | Conference Paper |

Predicting protein-protein interactions based on protein-domain relationships

Bing Wang;De-Shuang Huang;Peng Chen;Yunping Zhu;Yixue Li

Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005.

Year: 2005 | Conference Paper |

Cited by: Papers (3)

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This paper proposes a new method that can predict the interactions between proteins intermediated by the protein-domain relations. We utilize the lazy expectation maximization (LEM) to compute an improved maximization likelihood estimation (MLE) model. The protein-domain relationships are extruded from Flam database and the combined data set of Uetz and Ito are used as the source of protein-protei...Show More

Predicting protein-protein interactions based on protein-domain relationships

Bing Wang;De-Shuang Huang;Peng Chen;Yunping Zhu;Yixue Li

Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005.

Year: 2005 | Volume: 1 | Conference Paper |

Fast Modular network implementation for support vector machines

Guang-Bin Huang;K.Z. Mao;Chee-Kheong Siew;De-Shuang Huang

IEEE Transactions on Neural Networks

Year: 2005 | Volume: 16, Issue: 6 | Journal Article |

Cited by: Papers (36)

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Support vector machines (SVMs) have been extensively used. However, it is known that SVMs face difficulty in solving large complex problems due to the intensive computation involved in their training algorithms, which are at least quadratic with respect to the number of training examples. This paper proposes a new, simple, and efficient network architecture which consists of several SVMs each trai...Show More

Fast Modular network implementation for support vector machines

Guang-Bin Huang;K.Z. Mao;Chee-Kheong Siew;De-Shuang Huang

IEEE Transactions on Neural Networks

Year: 2005 | Volume: 16, Issue: 6 | Journal Article |

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)

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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 |

Shape matching using fuzzy discrete particle swarm optimization

Ji-Xiang Du;De-Shuang Huang;Jun Zhang;Xiao-Feng Wang

Proceedings 2005 IEEE Swarm Intelligence Symposium, 2005. SIS 2005.

Year: 2005 | Conference Paper |

Cited by: Papers (1)

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In this paper an efficient shape matching approach based on fuzzy discrete particle swarm optimization (FDPSO) is proposed. Based on fuzzy theory and PSO method, we applied this optimization method to a special combinatorial optimization problem: shape matching and recognition. Firstly, an original shape is approximated to a polygone and a shape representation of invariant attributes sequence is u...Show More

Shape matching using fuzzy discrete particle swarm optimization

Ji-Xiang Du;De-Shuang Huang;Jun Zhang;Xiao-Feng Wang

Proceedings 2005 IEEE Swarm Intelligence Symposium, 2005. SIS 2005.

Year: 2005 | Conference Paper |

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