Volume 10, Number 4

Efficacy of Non-negative Matrix Factorization for Feature Selection in Cancer Data

  Authors

Parth Patel¹, Kalpdrum Passi¹ and Chakresh Kumar Jain², ¹Laurentian University, Canada, ²Jaypee Institute of Information Technology, India

  Abstract

Over the past few years, there has been a considerable spread of microarray technology in many biological patterns, particularly in those pertaining to cancer diseases like leukemia, prostate, colon cancer, etc. The primary bottleneck that one experiences in the proper understanding of such datasets lies in their dimensionality, and thus for an efficient and effective means of studying the same, a reduction in their dimension to a large extent is deemed necessary. This study is a bid to suggesting different algorithms and approaches for the reduction of dimensionality of such microarray datasets.This study exploits the matrix-like structure of such microarray data and uses a popular technique called Non-Negative Matrix Factorization (NMF) to reduce the dimensionality, primarily in the field of biological data. Classification accuracies are then compared for these algorithms.This technique gives an accuracy of 98%.

  Keywords

Microarray datasets, Feature Extraction, Feature Selection, Principal Component Analysis, Non-negative Matrix Factorization, Machine learning.