Open Access
Solving the face recognition problem using QR factorization
TLDR
This paper considers computational complexity and efficacious of algorithm present a PCA/range(Sb) algorithm for dimensionality reduction of data, which transforms firstly the original space by using a basis of range(S b) and then in the transformed space applies PCA.Abstract:
Inspired and motivated by the idea of LDA/QR presented by Ye and Li, in addition, by the idea of WK- DA/QR and WKDA/SVD presented by Gao and Fan. In this paper, we first consider computational complexity and efficacious of algorithm present a PCA/range(Sb) algorithm for dimensionality reduction of data, which transforms firstly the original space by using a basis of range(Sb) and then in the transformed space applies PCA. Considering computationally expensive and time complexity, we further present an improved version of PCA/range(Sb), denot- ed by PCA/range(Sb)-QR, in which QR decomposition is used at the last step of PCA/range(Sb). In addition, we also improve LDA/GSVD, LDA/range(Sb) and PCA by means of QR decomposition. Extensive experiments on face images from UCI data sets show the effectiveness of the proposed algorithms.read more
Citations
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Journal ArticleDOI
Introduction to multivariate analysis, by C. Chatfield and A. J. Collins. Pp 246. £13 hardcover, £7·50 paperback. 1980. ISBN 0-412-16030-7/4 (Chapman and Hall)
TL;DR: In this paper, the multivariate normal distribution is used for principal component analysis and multivariate analysis of covariance and related topics, as well as multi-dimensional scaling and cluster analysis.
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Statistical Factor Analysis and Related Methods
TL;DR: In this article, statistical factor analysis and related methods are used for statistical quality analysis in the context of quality assurance. But they do not address the problem of privacy in the analysis.
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Median null(Sw)-based method for face feature recognition
TL;DR: A within-class scatter matrixnull space median method (M-N(S"w)), which first transforms the original space by employing a basis of within- class scatter matrix null space, and then in the transformed space the maximum of between-class scattered matrix is pursued.
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Local SVD based NIR face retrieval
TL;DR: The experimental results confirm the superiority of using S sub-band of SVD in terms of performance of the local descriptors over NIR face databases.
Journal ArticleDOI
PEM-PCA: a parallel expectation-maximization PCA face recognition architecture.
TL;DR: This research presents an alternative approach utilizing an Expectation-Maximization algorithm to reduce the determinant matrix manipulation resulting in the reduction of the stages' complexity, indicating lower complexity with an insignificant difference in recognition precision leading to high speed face recognition systems.
References
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The Elements of Statistical Learning: Data Mining, Inference, and Prediction
TL;DR: In this paper, the authors describe the important ideas in these areas in a common conceptual framework, and the emphasis is on concepts rather than mathematics, with a liberal use of color graphics.
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TL;DR: In this article, the authors present a graphical representation of data using Principal Component Analysis (PCA) for time series and other non-independent data, as well as a generalization and adaptation of principal component analysis.
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The Elements of Statistical Learning: Data Mining, Inference, and Prediction
TL;DR: The Elements of Statistical Learning: Data Mining, Inference, and Prediction as discussed by the authors is a popular book for data mining and machine learning, focusing on data mining, inference, and prediction.
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