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Mixtures of probabilistic principal component analyzers model high-dimensional nonlinear data by combining local linear models. Each mixture component is specifically designed to extract the local principal orientations in the data. An important issue with this generative model is its sensitivity to data lying off the low-dimensional manifold. In order to address this problem, the mixtures of robust...
Further to our recent work on the robust L1 PCA we introduce a new version of robust PCA model based on the so-called multivariate Laplace distribution (called L1 distribution) proposed in Eltoft et al. [2006. On the multivariate Laplace distribution. IEEE Signal Process. Lett. 13(5), 300–303]. Due to the heavy tail and high component dependency characteristics of the multivariate L1 distribution,...
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