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Learning Co-Sparse Analysis Operators with Separable Structures
In many applications, it is also required that the filter responses are obtained in a timely manner, which can be achieved by filters with a separable structure.
Separable Cosparse Analysis Operator Learning
The ability of having a sparse representation for a certain class of signals has many applications in data analysis, image processing, and other research fields.
On The Sample Complexity of Sparse Dictionary Learning
The main goal of this paper is to provide a sample complexity estimate that controls to what extent the empirical average deviates from the cost function.
Sample Complexity of Dictionary Learning and other Matrix Factorizations
Many modern tools in machine learning and signal processing, such as sparse dictionary learning, principal component analysis (PCA), non-negative matrix factorization (NMF), $K$-means clustering, etc., rely on the factorization of a matrix obtained by concatenating high-dimensional vectors from a training collection.
Separable Dictionary Learning
Many techniques in computer vision, machine learning, and statistics rely on the fact that a signal of interest admits a sparse representation over some dictionary.
