کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
411148 679182 2009 8 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Robust multivariate L1 principal component analysis and dimensionality reduction
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
پیش نمایش صفحه اول مقاله
Robust multivariate L1 principal component analysis and dimensionality reduction
چکیده انگلیسی

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, the proposed model is expected to be more robust against data outliers and fitting component dependency. Additionally, we demonstrate how a variational approximation scheme enables effective 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.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 72, Issues 4–6, January 2009, Pages 1242–1249
نویسندگان
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