Results 221 to 230 of about 26,367 (267)

K-SVD Meets Transform Learning: Transform K-SVD

IEEE Signal Processing Letters, 2014
Recently there has been increasing attention directed towards the analysis sparsity models. Consequently, there is a quest for learning the operators which would enable analysis sparse representations for signals in hand. Analysis operator learning algorithms such as the Analysis K-SVD have been proposed.
Ender M Ekşioğlu
exaly   +2 more sources

Regularized K-SVD

IEEE Signal Processing Letters, 2017
The problem of dictionary learning (DL) for sparse representations can be approximately solved by several algorithms. Regularization of the optimization objective (representation error) was proved useful, since it avoids possible bottlenecks due to nearly linearly dependent atoms.
Bogdan Dumitrescu, Paul Irofti
openaire   +1 more source

Compressive K-SVD

2013 IEEE International Conference on Acoustics, Speech and Signal Processing, 2013
Dictionary learning algorithms design a dictionary that is specifically tailored to enable sparse representation of a given set of training signals. In turn, the increased sparsity of the signals with respect to this dictionary enables significantly improved performance in a variety of state-of-the-art signal processing tasks, e.g. compressive sensing.
Farhad Pourkamali-Anaraki, Shannon M. Hughes   +1 more
openaire   +1 more source

Information filtering using the Riemannian SVD (R-SVD)

1998
The Riemannian SVD (or R-SVD) is a recent nonlinear generalization of the SVD which has been used for specific applications in systems and control. This decomposition can be modified and used to formulate a filtering-based implementation of Latent Semantic Indexing (LSI) for conceptual information retrieval.
Eric P. Jiang, Michael W. Berry
openaire   +1 more source

A general framework for SVD flows and joint SVD flows

2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03)., 2003
The paper presents a general framework for the development of continuous algorithms for SVD and joint SVD problems. The framework for SVD is derived based on gradient flows on unitary groups. Two previous examples of SVD flows discovered heuristically are derived systematically using the framework.
openaire   +1 more source

A Systolic Array for SVD Updating

SIAM Journal on Matrix Analysis and Applications, 1993
A singular value decomposition (SVD) updating algorithm supplemented with a certain re-orthogonalization scheme is implemented on a systolic array with \(O(n^ 2)\) parallelism for \(O(n^ 2)\) complexity, by combining systolic implementations for the matrix-vector product, the QR updating and the SVD. It is shown that a main computational bottleneck for
Marc Moonen, Paul Van Dooren, Joos Vandewalle   +2 more
openaire   +1 more source

Contrast Enhancement of an Image by DWT-SVD and DCT-SVD

2017
In this paper a novel contrast stretching technique is proposed that is based on two methods: (a) Discrete Wavelet Transform (DWT) followed by SVD and (b) Discrete Cosine Transform (DCT) followed by SVD where SVD refers to Singular Value Decomposition.
Sugandha Juneja, Rohit Anand
openaire   +1 more source

Pairwise Approximate K-SVD

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2019
Pairwise, or separable, dictionaries are suited for the sparse representation of 2D signals in their original form, without vectorization. They are equivalent with enforcing a Kronecker structure on a standard dictionary for 1D signals. We present a dictionary learning algorithm, in the coordinate descent style of Approximate K-SVD, for such ...
Paul Irofti, Bogdan Dumitrescu
openaire   +1 more source