Title :
Non-negative matrix and tensor factorization based classification of clinical microarray gene expression data
Author :
Li, Yifeng ; Ngom, Alioune
Author_Institution :
Sch. of Comput. Sci., Univ. of Windsor, Windsor, ON, Canada
Abstract :
Non-negative information can benefit the analysis of microarray data. This paper investigates the classification performance of non-negative matrix factorization (NMF) over gene-sample data. We also extends it to higher-order version for classification of clinical time-series data represented by tensor. Experiments show that NMF and the higher-order NMF can achieve at least comparable prediction performance.
Keywords :
bioinformatics; genetics; matrix decomposition; molecular biophysics; tensors; clinical microarray gene expression data; clinical time series data; gene-sample data; higher-order NMF; nonnegative information; nonnegative matrix factorization; tensor factorization; Accuracy; Feature extraction; Matrix decomposition; Principal component analysis; Tensile stress; Training; GST data; HONMF; NMF; tensor decomposition;
Conference_Titel :
Bioinformatics and Biomedicine (BIBM), 2010 IEEE International Conference on
Conference_Location :
Hong Kong
Print_ISBN :
978-1-4244-8306-8
Electronic_ISBN :
978-1-4244-8307-5
DOI :
10.1109/BIBM.2010.5706606
