Constrained low-rank matrix approximations have been known for decades as powerful
linear dimensionality reduction techniques able to extract the information contained in large …

Deep neural networks provide unprecedented performance gains in many real-world
problems in signal and image processing. Despite these gains, the future development and …

Although nonnegative matrix factorization (NMF) is widely used, some matrix factorization
methods result in misleading results and waste of computing resources due to lack of timely …

Model-based methods and deep neural networks have both been tremendously successful
paradigms in machine learning. In model-based methods, problem domain knowledge can …

This paper deals with the separation of music into individual instrument tracks which is
known to be a challenging problem. We describe two different deep neural network …

Given a network, intuitively two nodes belong to the same role if they have similar structural
behavior. Roles should be automatically determined from the data, and could be, for …

Energy disaggregation is the task of taking a whole-home energy signal and separating it
into its component appliances. Studies have shown that having device-level energy …

This paper addresses the estimation of the latent dimensionality in nonnegative matrix
factorization (NMF) with the (β)--divergence. The (β)-divergence is a family of cost functions …

We apply machine learning techniques to the problem of separating multiple speech
sources from a single microphone recording. The method of choice is a sparse non-negative …

Two popular representation learning paradigms are dictionary learning and deep learning.
While dictionary learning focuses on learning “basis” and “features” by matrix factorization …