Orthogonal Non-Negative Matrix Factorization using Ridge Term for Classifying Expressed Gene

This research provides the development of Non-Negative Matrix Factorization method for classifying gene expression data. Furthermore, we also compare the NMF method with Uni-Orthogonal NMF for classifying the data. We use two schemas of datasets which use complete dataset and incomplete dataset. The incomplete dataset contains missing values which used to prove that the methods can handle the missing values problem on the classification problem. Sparse regularization in dimensionality reduction using ridge term (Lz-Norm) is applied in this study to see the effect of sparse regularization in the incomplete dataset. In this paper, we propose an approach to classify the diseases from gene expression data using the combination of matrix factorization methods and support vector machine (SVM). The experimental results show that adding ridge term in Y-orthogonal NMF make accuracy higher in training data with incomplete data. Y-orthogonal NMF using ridge term is the best method for classifying expressed genes with incomplete data than the other compared methods.