Applications of matrix factorization methods to climate data

Polynomial Optimization Methods for Matrix Factorization

Matrix factorization is a core technique in many machine learning problems, yet also presents a nonconvex and often difficult-to-optimize problem. In this paper we present an approach based upon polynomial optimization techniques that both improves the convergence time of matrix factorization algorithms and helps them escape from local optima. Our method is based on the realization that given a...

Descent methods for Nonnegative Matrix Factorization

In this paper, we present several descent methods that can be applied to nonnegative matrix factorization and we analyze a recently developped fast block coordinate method called Rank-one Residue Iteration (RRI). We also give a comparison of these different methods and show that the new block coordinate method has better properties in terms of approximation error and complexity. By interpreting...

Relation Extraction using Matrix Factorization Methods

Relation extraction has an important role within the information extraction domain. Given an initial ontology specifying noun categories, instances from these categories and text corpora, the relation extraction task consists of extracting the relations that connect instances from these categories. Current research works related with this topic, are mostly based on the clustering methods. One w...

Projected Gradient Methods for Nonnegative Matrix Factorization

Nonnegative matrix factorization (NMF) can be formulated as a minimization problem with bound constraints. Although bound-constrained optimization has been studied extensively in both theory and practice, so far no study has formally applied its techniques to NMF. In this letter, we propose two projected gradient methods for NMF, both of which exhibit strong optimization properties. We discuss ...

Sparse Network - regularized Nonnegative Matrix Factorization and Applications to Tumor