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Showing 1-25 of 34,904 resultsfor "Index Terms":Principal Component Analysis
"Index Terms":Principal Component Analysis
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Face recognition using IPCA-ICA algorithm
In this paper, a fast incremental principal non-Gaussian directions analysis algorithm, called IPCA-ICA, is introduced. This algorithm computes the principal components of a sequence of image vectors incrementally without estimating the covariance matrix (so covariance-free) and at the same time transforming these principal components to the independent directions that maximize the non-Gaussianity...Show More
Delay Correlation Subspace Decomposition Algorithm and Its Application in fMRI
This paper reports a new delay subspace decomposition (DSD) algorithm. Instead of using the canonical zero-delay correlation matrix, the new DSD algorithm introduces a delay into the correlation matrix of the subspace decomposition to suppress noises in the data. The algorithm is applied to functional magnetic resonance imaging (fMRI) to detect the regions of focal activities in the brain. The eff...Show More
Robust Principal Component Analysis Based on Maximum Correntropy Criterion
Principal component analysis (PCA) minimizes the mean square error (MSE) and is sensitive to outliers. In this paper, we present a new rotational-invariant PCA based on maximum correntropy criterion (MCC). A half-quadratic optimization algorithm is adopted to compute the correntropy objective. At each iteration, the complex optimization problem is reduced to a quadratic problem that can be efficie...Show More
Fine-Scale Genetic Mapping Using Independent Component Analysis
The aim of genetic mapping is to locate the loci responsible for specific traits such as complex diseases. These traits are normally caused by mutations at multiple loci of unknown locations and interactions. In this work, we model the biological system that relates DNA polymorphisms with complex traits as a linear mixing process. Given this model, we propose a new fine-scale genetic mapping metho...Show More
Incremental Kernel Principal Component Analysis
The kernel principal component analysis (KPCA) has been applied in numerous image-related machine learning applications and it has exhibited superior performance over previous approaches, such as PCA. However, the standard implementation of KPCA scales badly with the problem size, making computations for large problems infeasible. Also, the “batch” nature of the standard KPCA computation method do...Show More
Comparison of atrial signal extraction algorithms in 12-lead ECGs with atrial fibrillation
P. Langley;J.J. Rieta;M. Stridh;J. Millet;L. Sornmo;A. Murray
Analysis of atrial rhythm is important in the treatment and management of patients with atrial fibrillation. Several algorithms exist for extracting the atrial signal from the electrocardiogram (ECG) in atrial fibrillation, but there are few reports on how well these techniques are able to recover the atrial signal. We assessed and compared three algorithms for extracting the atrial signal from th...Show More
Feature Extraction of Epileptic EEG using Wavelet Power Spectra and Functional PCA
2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Year: 2019 | Conference Paper |
Cited by: Papers (2)
In this work, we propose a novel approach to analyze Epileptic EEG signals using wavelet power spectra and functional principal component analysis. Both continuous and discrete wavelet power spectra are considered. By transforming EEG signals into power spectra, we significantly enhance the functionality of random signals, which makes functional principal component analysis be a suitable technique...Show More
On Feature Extraction via Kernels
Using the kernel trick idea and the kernels-as-features idea, we can construct two kinds of nonlinear feature spaces, where linear feature extraction algorithms can be employed to extract nonlinear features. In this correspondence, we study the relationship between the two kernel ideas applied to certain feature extraction algorithms such as linear discriminant analysis, principal component analys...Show More
Comments on "On Image Matrix Based Feature Extraction Algorithms
A class of image-matrix-based feature extraction algorithms has been discussed earlier. The correspondence argues that 2D principal component analysis and Fisher linear discriminant (FLD) are equivalent to block-based PCA and FLD. In this correspondence, we point out that this statement is not rigorous.Show More
Determination of the Number of Principal Directions in a Biologically Plausible PCA Model
Adaptively determining an appropriate number of principal directions for principal component analysis (PCA) neural networks is an important problem to address when one uses PCA neural networks for online feature extraction. In this letter, inspired from biological neural networks, a single-layer neural network model with lateral connections is proposed which uses an improved generalized Hebbian al...Show More
Binary Two-Dimensional PCA
Fast training and testing procedures are crucial in biometrics recognition research. Conventional algorithms, e.g., principal component analysis (PCA), fail to efficiently work on large-scale and high-resolution image data sets. By incorporating merits from both two-dimensional PCA (2DPCA)-based image decomposition and fast numerical calculations based on Haarlike bases, this technical corresponde...Show More
Two Dimensional Principal Component Analysis based Independent Component Analysis for face recognition
2011 International Conference on Multimedia Technology
Year: 2011 | Conference Paper |
Cited by: Papers (14)
We usually reduce the dimensionalities of the data before running many algorithms of processing images and audio. Then we can remove the redundant data and reserve the useful features for future analysis. Independent Component Analysis is a famous dimensionality reduction algorithm. We usually run Principal Component Analysis algorithm firstly as a preprocessing procedure for decreasing the comput...Show More
Nonlinear principal component analysis for seismic data compression
2012 1st International Conference on Recent Advances in Information Technology (RAIT)
Year: 2012 | Conference Paper |
Seismic data processing to interpret subsurface features is both computationally and data intensive. It is necessary to keep the dimensionality of data as small as possible, for good generalization from limited data. Therefore it is worthwhile exploring methods to compress the size of seismic data. In this paper, we consider approaches for linear and nonlinear principal component analysis (PCA) me...Show More
Sparse approximation of long-term biomedical signals for classification via dynamic PCA
2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Year: 2011 | Conference Paper |
Cited by: Papers (1)
Sparse approximation is a novel technique in applications of event detection problems to long-term complex biomedical signals. It involves simplifying the extent of resources required to describe a large set of data sufficiently for classification. In this paper, we propose a multivariate statistical approach using dynamic principal component analysis along with the non-overlapping moving window t...Show More
L1-Norm-Based 2DPCA
In this paper, we first present a simple but effective L1-norm-based two-dimensional principal component analysis (2DPCA). Traditional L2-norm-based least squares criterion is sensitive to outliers, while the newly proposed L1-norm 2DPCA is robust. Experimental results demonstrate its advantages.Show More
Analysis of sparse PCA using high dimensional data
Fatin Raihana On;Rozita Jailani;Siti Lailatul Hassan;Nooritawati Md Tahir
2016 IEEE 12th International Colloquium on Signal Processing & Its Applications (CSPA)
Year: 2016 | Conference Paper |
Cited by: Papers (6)
In this study the Sparse Principal Component Analysis (PCA) has been chosen as feature extraction and further compared with the conventional PCA technique with six UCI Machine Learning high dimensionality data as database. Results attained showed that both PCA and Sparse PCA techniques are indeed suitable as feature extraction for high dimensional data since the accuracy rate attained are higher a...Show More
Automated Nonlinear Feature Generation and Classification of Foot Pressure Lesions
Tingting Mu;Todd C. Pataky;Andrew H. Findlow;Min S. H. Aung;John Yannis Goulermas
Plantar lesions induced by biomechanical dysfunction pose a considerable socioeconomic health care challenge, and failure to detect lesions early can have significant effects on patient prognoses. Most of the previous works on plantar lesion identification employed the analysis of biomechanical microenvironment variables like pressure and thermal fields. This paper focuses on foot kinematics and a...Show More
Symmetric Subspace Learning for Image Analysis
Subspace learning (SL) is one of the most useful tools for image analysis and recognition. A large number of such techniques have been proposed utilizing a priori knowledge about the data. In this paper, new subspace learning techniques are presented that use symmetry constraints in their objective functions. The rational behind this idea is to exploit the a priori knowledge that geometrical symme...Show More
Principal Component Analysis of Vertical Ground Reaction Force: A Powerful Method to Discriminate Normal and Abnormal Gait and Assess Treatment
2006 International Conference of the IEEE Engineering in Medicine and Biology Society
Year: 2006 | Conference Paper |
Cited by: Papers (12)
This study aims at testing the application of principal component analysis (PCA) in the ground reaction force (GRF) in discriminating the gait pattern between normal and abnormal subjects, and assessing the rehabilitation treatment. The sample was composed by 31 subjects, organized into two groups: a control group (CG) of 25 normal and a group (FG) of six patients with lower limb fractures, which ...Show More
Neural network based intrusion detection using Bayesian with PCA and KPCA feature extraction
2015 IEEE International Conference on Computer Graphics, Vision and Information Security (CGVIS)
Year: 2015 | Conference Paper |
Cited by: Papers (7)
In today's digital world use of computer systems, computer networks and internet are increasing rapidly. Due to this, information is processed digitally. So, providing effective security to such digital data is crucial task. There are some tools and systems are available for the security of digital information. From these tools and systems intrusion detection system is an important method. It is n...Show More
Face Recognition Using an Enhanced Independent Component Analysis Approach
This paper is concerned with an enhanced independent component analysis (ICA) and its application to face recognition. Typically, face representations obtained by ICA involve unsupervised learning and high-order statistics. In this paper, we develop an enhancement of the generic ICA by augmenting this method by the Fisher linear discriminant analysis (LDA); hence, its abbreviation, FICA. The FICA ...Show More
A Least-Squares Framework for Component Analysis
Over the last century, Component Analysis (CA) methods such as Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Canonical Correlation Analysis (CCA), Locality Preserving Projections (LPP), and Spectral Clustering (SC) have been extensively used as a feature extraction step for modeling, classification, visualization, and clustering. CA techniques are appealing because many c...Show More
Representing Images Using Nonorthogonal Haar-Like Bases
The efficient and compact representation of images is a fundamental problem in computer vision. In this paper, we propose methods that use Haar-like binary box functions to represent a single image or a set of images. A desirable property of these box functions is that their inner product operation with an image can be computed very efficiently. We propose two closely related novel subspace method...Show More
Links between PPCA and subspace methods for complete Gaussian density estimation
High-dimensional density estimation is a fundamental problem in pattern recognition and machine learning areas. In this letter, we show that, for complete high-dimensional Gaussian density estimation, two widely used methods, probabilistic principal component analysis and a typical subspace method using eigenspace decomposition, actually give the same results. Additionally, we present a unified vi...Show More
BDPCA plus LDA: a novel fast feature extraction technique for face recognition
Appearance-based methods, especially linear discriminant analysis (LDA), have been very successful in facial feature extraction, but the recognition performance of LDA is often degraded by the so-called "small sample size" (SSS) problem. One popular solution to the SSS problem is principal component analysis(PCA)+LDA (Fisherfaces), but the LDA in other low-dimensional subspaces may be more effecti...Show More
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