Bilinear probabilistic principal component analysis

Jianhua ZHAO, Leung Ho Philip YU, James T. KWOK

Research output: Contribution to journalArticlespeer-review

20 Citations (Scopus)

Abstract

Probabilistic principal component analysis (PPCA) is a popular linear latent variable model for performing dimension reduction on 1-D data in a probabilistic manner. However, when used on 2-D data such as images, PPCA suffers from the curse of dimensionality due to the subsequently large number of model parameters. To overcome this problem, we propose in this paper a novel probabilistic model on 2-D data called bilinear PPCA (BPPCA). This allows the establishment of a closer tie between BPPCA and its nonprobabilistic counterpart. Moreover, two efficient parameter estimation algorithms for fitting BPPCA are also developed. Experiments on a number of 2-D synthetic and real-world data sets show that BPPCA is more accurate than existing probabilistic and nonprobabilistic dimension reduction methods. Copyright © 2012 IEEE.
Original languageEnglish
Pages (from-to)492-503
JournalIEEE Transactions on Neural Networks and Learning Systems
Volume23
Issue number3
Early online dateJan 2012
DOIs
Publication statusPublished - Mar 2012

Citation

Zhao, J., Yu, P. L. H., & Kwok, J. T. (2012). Bilinear probabilistic principal component analysis. IEEE Transactions on Neural Networks and Learning Systems, 23(3), 492-503. doi: 10.1109/TNNLS.2012.2183006

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