Crucially, an online learning procedure is employed, allowing scalability to very large datasets which would be beyond the capabilities of existing batch ...

The problem of learning a data-adaptive dictionary for a very large collection of signals is addressed. This paper proposes a statistical.

A statistical model and associated variational Bayesian inference for simultaneously learning the dictionary and performing sparse coding of the signals is ...

The problem of learning a data-adaptive dictionary for a very large collection of signals is addressed. This paper proposes a statistical model and ...

Crucially, an online learning procedure is employed, allowing scalability to very large datasets which would be beyond the capabilities of existing batch ...

As many modern applications involve complex data and/or sampling mechanisms, it is often challenging to design inter- pretable and tractable Bayesian models. We ...

Mar 7, 2015 · Abstract—We consider a dictionary learning problem whose objective is to design a dictionary such that the signals admits a.

This example uses a large dataset of faces to learn a set of 20 x 20 images patches that constitute faces.

Non-parametric Bayesian techniques are considered for learning dictionaries for sparse image representations, with applications in denoising, inpainting and ...

The model is adapted for online learning, which allows dictionary learning on large data sets. After KFA is introduced, the model is extended to a deep convolu-.