title: Bilinear probabilistic principal component analysis.
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.
authors.forename: Jianhua
authors.initials: J
authors.lastname: Zhao
authors.forename: Philip L H
authors.initials: PL
authors.lastname: Yu
authors.forename: James T
authors.initials: JT
authors.lastname: Kwok