NDLI: Bayesian Matrix Factorization for Face Recognition

Content Provider	IEEE Xplore Digital Library
Author	Jiang Bian Xinbo Gao Xiumei Wang
Copyright Year	2013
Description	Author affiliation: Sch. of Electron. Eng., Xidian Univ., Xi'an, China (Jiang Bian; Xinbo Gao; Xiumei Wang)
Abstract	Principal Component Analysis (PCA), one of the most popular dimensionality reduction algorithms, has three particular problems: the number of eigenvalues is limited by the two direction dimensions; it assumes for reconstruction of Gaussian distributed data, not for classification problems; it assumes that eigenvalues and eigenvectors is all linear. In this paper, we proposed a Bayesian Mixture Model, Bayesian Mixture of Inverse Regression (BMI), to deal with these three problems as preprocessing method and then use classic algorithms, Discriminative Locality Alignment (DLA) and Fishers Linear Discriminant Analysis (FLDA), to classify the test data into different topics. Through empirical studies on the face recognition demonstrate the effectiveness of DLA & BMI and LDA & BMI are more effective than DLA & PCA and LDA & PCA.
Sponsorship	IEEE Syst., Man, Cybern. Soc.
Starting Page	2109
Ending Page	2113
File Size	428954
Page Count	5
File Format	PDF
ISBN	9781479906529
DOI	10.1109/SMC.2013.361
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-10-13
Publisher Place	UK
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Principal component analysis Bayes methods Data models Face recognition Manifolds Data mining Eigenvalues and eigenfunctions dimensionality reduction Bayesian Mixture Model
Content Type	Text
Resource Type	Article

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