Hypergraph Regularized Discriminative Nonnegative Matrix Factorization on Sample Classification and Co-Differentially Expressed Gene Selection

Local Learning Regularized Nonnegative Matrix Factorization

Nonnegative Matrix Factorization (NMF) has been widely used in machine learning and data mining. It aims to find two nonnegative matrices whose product can well approximate the nonnegative data matrix, which naturally lead to parts-based representation. In this paper, we present a local learning regularized nonnegative matrix factorization (LLNMF) for clustering. It imposes an additional constr...

EquiNMF: Graph Regularized Multiview Nonnegative Matrix Factorization

Nonnegative matrix factorization (NMF) methods have proved to be powerful across a wide range of real-world clustering applications. Integrating multiple types of measurements for the same objects/subjects allows us to gain a deeper understanding of the data and refine the clustering. We have developed a novel Graph-reguarized multiview NMF-based method for data integration called EquiNMF. The ...

Automated Gene Classification using Nonnegative Matrix Factorization on Biomedical Literature

Understanding functional gene relationships is a challenging problem for biological applications. High-throughput technologies such as DNA microarrays have inundated biologists with a wealth of information, however, processing that information remains problematic. To help with this problem, researchers have begun applying text mining techniques to the biological literature. This work extends pr...

Sparse Network - regularized Nonnegative Matrix Factorization and Applications to Tumor

Group Nonnegative Matrix Factorization for EEG Classification

Given electroencephalogram (EEG) data measured from several subjects under the same conditions, our goal is to estimate common task-related bases in a linear model that capture intra-subject variations as well as inter-subject variations. Such bases capture the common phenomenon in group data, which is a core of group analysis. In this paper we present a method of nonnegative matrix factorizati...