Abstract
Further to our recent work on the robust L1 PCA we introduce a new ver-sion of robust PCA model based on the so-called multivariate Laplace distribution(called L1 distribution) proposed in (Eltoft et al., 2006). Due to the heavy tail and high component dependency characteristics of the multivariate L1 distribution, the proposed model is expected to be more robust against data outliers and ¯ttingcomponent dependency. Additionally, we demonstrate how a variational approx-imation scheme enables e®ective inference of key parameters in the probabilistic multivariate L1-PCA model. By doing so, a tractable Bayesian inference can be achieved based on the variational EM-type algorithm.
Original language | English |
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Pages (from-to) | 1242-1249 |
Number of pages | 8 |
Journal | Neurocomputing |
Volume | 72 |
Issue number | 4-6 |
DOIs | |
Publication status | Published - 2009 |