Exponentiated gradient versus gradient descent for linear predictors
Information and Computation
An Introduction to Copulas (Springer Series in Statistics)
An Introduction to Copulas (Springer Series in Statistics)
Projected Gradient Methods for Nonnegative Matrix Factorization
Neural Computation
Document clustering using nonnegative matrix factorization
Information Processing and Management: an International Journal
Non-negative matrix factorization on Kernels
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
Nonnegative Matrix and Tensor Factorizations: Applications to Exploratory Multi-way Data Analysis and Blind Source Separation
A generalization of independence in naive bayes model
IDEAL'10 Proceedings of the 11th international conference on Intelligent data engineering and automated learning
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Nonnegative Matrix Factorization (NMF) is broadly used as a mathematical tool for processing tasks of tabulated data. In this paper, an extension of NMF based on a generalized product rule, defined with a nonlinear one-parameter function and its inverse, is proposed. From a viewpoint of subspace methods, the extended NMF constructs flexible subspaces which plays an important role in classification tasks. Experimental results on benchmark datasets show that the proposed extension improves classification accuracies.