Protein functional properties prediction in sparsely-label PPI networks through regularized non-negative matrix factorization

Graph Regularized Non-negative Matrix Factorization By Maximizing Correntropy

Non-negative matrix factorization (NMF) has proved effective in many clustering and classification tasks. The classic ways to measure the errors between the original and the reconstructed matrix are l2 distance or KullbackLeibler (KL) divergence. However, nonlinear cases are not properly handled when we use these error measures. As a consequence, alternative measures based on nonlinear kernels,...

Regularized Non-Negative Matrix Factorization for Dynamic and Relational Data

Data involving repeated measurements of several variables over different factors, experimental conditions or time may exhibit correlations among variables, as well as between factors. The discovery of these underlying, meaningful relations is important to a wide variety of areas such as psychology, signal processing, finance, among others. Common methods such as independent component analysis, ...

Learning Sparse Basis Vectors in Small-Sample Datasets through Regularized Non-Negative Matrix Factorization

This article presents a novel dimensionality-reduction technique, Regularized Non-negative Matrix Factorization (RNMF), which combines the non-negativity constraint of NMF with a regularization term. In contrast with NMF, which degrades to holistic representations with decreasing amount of data, RNMF is able to extract parts of objects even in the small-sample case.

Non-negative Matrix Factorization in Multimodality Data for Segmentation and Label Prediction

With the increasing availability of annotated multimedia data on the Internet, techniques are in demand that allow for a principled joint processing of different types of data. Multiview learning and multiview clustering attempt to identify latent components in different features spaces in a simultaneous manner. The resulting basis vectors or centroids faithfully represent the different views o...

Non-negative matrix factorization