Extraction of functional domains in optical imaging data using regularized nonnegative matrix factorization

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 ...

Sparse Network - regularized Nonnegative Matrix Factorization and Applications to Tumor

Analysis of two-mode network data using nonnegative matrix factorization

Nonnegative matrix factorization has been offered as a fast and effective method for analyzing nonnegative two-mode proximity data. The goal is to structurally represent a nonnegative proximity matrix as the product of two lower-dimensional nonnegative matrices. Goodness of fit is typically measured as the sum of the squared deviations between the observed matrix elements and the estimated elem...

Nonnegative Matrix Factorization for Spectral Data Analysis

Data analysis is pervasive throughout business, engineering and science. Very often the data to be analyzed is nonnegative, and it is often preferable to take this constraint into account in the analysis process. Here we are concerned with the application of analyzing data obtained using astronomical spectrometers, which provide spectral data which is inherently nonnegative. The identification ...