ICML 2011 workshop on unsupervised
and transfer learning
Unsupervised
dimensionality reduction via gradient-based matrix factorization with
two learning rates and their automatic updates
Vladimir
Nikulin and Tian-Hsiang Huang
Department of Mathematics, University of Queensland, Australia
The high dimensionality of the data, the expressions of thousands of
features in a much smaller number of samples, presents challenges that
affect applicability of the analytical results. In principle, it would
be better to describe the data in terms of a small number of
meta-features, derived as a result of matrix factorization, which could
reduce noise while still capturing the essential features of the data.
Three novel and mutually relevant methods are presented in this paper:
1) gradient-based matrix factorization with two learning rates (in
accordance with the number of factor matrices) and their automatic
updates; 2) nonparametric criterion for the selection of the number of
factors; and 3) nonnegative version of the gradient-based matrix
factorization which doesn’t require any extra computational costs in
difference to the existing methods. We demonstrate an effectiveness of
the proposed methods to the supervised classification of gene
expression data.