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Published on: October 11, 2019

On alpha-divergence based nonnegative matrix factorization for clustering cancer gene expression data.

Weixiang Liu¹, Kehong Yuan, Datian Ye

¹Research Center of Biomedical Engineering, Life Science Division, Graduate school at Shenzhen, Tsinghua University, Shenzhen 518055, China. victorwxliu@yahoo.com

Artificial Intelligence in Medicine

|July 8, 2008

Summary

This study explores optimal alpha values for Nonnegative Matrix Factorization (NMF) clustering using alpha-divergence. Results indicate alpha=1 and alpha=2 are optimal for cancer gene expression data analysis.

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Area of Science:

Bioinformatics
Computational Biology
Data Science

Background:

Nonnegative Matrix Factorization (NMF) is a robust technique for data clustering.
Recent advancements introduced NMF variants utilizing alpha-divergence.
Determining the optimal alpha parameter for these NMF methods remains an open challenge.

Purpose of the Study:

To experimentally determine the optimal alpha parameter for alpha-divergence-based NMF algorithms.
To evaluate the performance of different alpha values in clustering cancer gene expression data.

Main Methods:

The study employed an NMF variant incorporating alpha-divergence.
Eleven cancer gene expression datasets were utilized for experimental analysis.
Various alpha values were systematically tested to identify optimal parameters.

Main Results:

Experimental results demonstrated that specific alpha values yield superior clustering performance.
Alpha values of 1 and 2 were identified as particularly effective for the tested datasets.
The findings provide empirical evidence for optimal parameter selection in NMF applications.

Conclusions:

The optimal alpha parameter for alpha-divergence NMF is dataset-dependent but shows consistent effectiveness for certain values.
Alpha=1 and alpha=2 represent special optimal cases for practical applications in cancer gene expression clustering.
This research offers valuable guidance for selecting NMF parameters in bioinformatics and data mining.