Latent Variable Models for Dimensionality Reduction
نویسندگان
چکیده
Principal coordinate analysis (PCO), as a duality of principal component analysis (PCA), is also a classical method for exploratory data analysis. In this paper we propose a probabilistic PCO by using a normal latent variable model in which maximum likelihood estimation and an expectation-maximization algorithm are respectively devised to calculate the configurations of objects in a lowdimensional Euclidean space. We also devise probabilistic formulations for kernel PCA which is a nonlinear extension of PCA.
منابع مشابه
Description of current research
This document describes the research I have done since October 1995 for my PhD thesis at the Dept. of Computer Science, University of Sheffield, U.K., which I expect to complete by October/November 1999. My thesis research has involved two generic fields of machine learning: dimensionality reduction and sequential data reconstruction, which I have approached from the common point of view of lat...
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