Nesterov's iterations for NMF-Based supervised classification of texture patterns

Authors:
Rafal Zdunek;Zhaoshui He
Affiliations:
Institute of Telecommunications, Teleinformatics and Acoustics, Wroclaw University of Technology, Wroclaw, Poland;Faculty of Automation, Guangdong University of Technology, Guangzhou, China
Venue:
LVA/ICA'12 Proceedings of the 10th international conference on Latent Variable Analysis and Signal Separation
Year:
2012

Citing 8
Cited 0

Object Recognition from Local Scale-Invariant Features

ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Projected Gradient Methods for Nonnegative Matrix Factorization

Neural Computation
Unsupervised texture classification: Automatically discover and classify texture patterns

Image and Vision Computing
Nonnegative Matrix Factorization Based on Alternating Nonnegativity Constrained Least Squares and Active Set Method

SIAM Journal on Matrix Analysis and Applications
A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems

SIAM Journal on Imaging Sciences
A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems

SIAM Journal on Imaging Sciences
Nonnegative Matrix and Tensor Factorizations: Applications to Exploratory Multi-way Data Analysis and Blind Source Separation

Nonnegative Matrix and Tensor Factorizations: Applications to Exploratory Multi-way Data Analysis and Blind Source Separation
NESVM: A Fast Gradient Method for Support Vector Machines

ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining

Quantified Score

Hi-index	0.00

Visualization

Abstract

Nonnegative Matrix Factorization (NMF) is an efficient tool for a supervised classification of various objects such as text documents, gene expressions, spectrograms, facial images, and texture patterns. In this paper, we consider the projected Nesterov's method for estimating nonnegative factors in NMF, especially for classification of texture patterns. This method belongs to a class of gradient (first-order) methods but its convergence rate is determined by O(1/k2). The classification experiments for the selected images taken from the UIUC database demonstrate a high efficiency of the discussed approach.