  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%.
