Partially Observable Markov Decision Processes (POMDPs) are a well-established and rigorous framework for sequential decision-making under uncertainty. POMDPs are well-known to be...
We present a technique for denoising speech using nonnegative matrix factorization (NMF) in combination with statistical speech and noise models. We compare our new technique to s...
Kevin W. Wilson, Bhiksha Raj, Paris Smaragdis, Aja...
Non-negative matrix factorization (NMF) is a powerful feature extraction method for finding parts-based, linear representations of non-negative data . Inherently, it is unsupervis...
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. We als...
The nonnegative matrix factorization (NMF) has been shown recently to be useful for clustering. Various extensions of NMF have also been proposed. In this paper we present an over...