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K-SVD Meets Transform Learning: Transform K-SVD
IEEE Signal Processing Letters, 2014Recently 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. Eksioglu, Ozden Bayir
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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
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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
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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 +1 more
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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 +1 more
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Information filtering using the Riemannian SVD (R-SVD)
1998The 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
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Laguerre-SVD reduced order modeling
IEEE 8th Topical Meeting on Electrical Performance of Electronic Packaging (Cat. No.99TH8412), 2000A reduced-order modeling method based on a system description in terms of orthonormal Laguerre functions, together with a Krylov subspace decomposition technique is presented. The link with Pade approximation, the block Arnoldi process and singular value decomposition (SVD) leads to a simple and stable implementation of the algorithm. Novel features of
L. Knockaert, D. De Zutter
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2013
The issue of separability is of major importance when using the Proper Generalized Decomposition. Efficient computer implementations require the separated representation of model parameters, boundary conditions and/or source terms. This can be performed by applying the Singular Value Decomposition (SVD) or its multi-dimensional counterpart, the so ...
Francisco Chinesta +2 more
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The issue of separability is of major importance when using the Proper Generalized Decomposition. Efficient computer implementations require the separated representation of model parameters, boundary conditions and/or source terms. This can be performed by applying the Singular Value Decomposition (SVD) or its multi-dimensional counterpart, the so ...
Francisco Chinesta +2 more
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Performance evaluation of RDWT-SVD and DWT-SVD watermarking schemes
AIP Conference Proceedings, 2016Digital image watermarking protects content by embedding a signal (i.e., owner information) into the host image without noticeable degradation in visual quality. To develop any image watermarking scheme, there some important requirements should be achieved such as imperceptibly, robustness, capacity, security, and, etc.
Taha H. Rassem +2 more
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Performance Evaluation of Robust Watermarking Using DWT-SVD and RDWT-SVD
2019 6th International Conference on Signal Processing and Integrated Networks (SPIN), 2019DWT and RDWT are two prominent techniques for digital watermarking. The two key limitations associated with DWT method are: the shift variance property, and the size of original image gets decreased at every decomposition level. These limitations lead to a reduction in data payload offered by the watermarking system. RDWT succeeds in dealing with these
Poonam Kadian +2 more
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SVD—based approximations of bivariate functions
2005 IEEE International Symposium on Circuits and Systems, 2005A method to approximate functions of two variables is presented; it is suitable for hardware implementations based on digital or mixed signal architectures. Such a method is based on the properties of the singular value decomposition (SVD) of a matrix that stores the samples of the function to be approximated.
F. Bizzarri +2 more
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SVD and DWT-SVD Domain Robust Watermarking using Differential Evolution Algorithm
2009This study aims to develop two optimal watermarking techniques based on SVD and DWT-SVD domain for grey-scale images. The first one embeds the watermark by modifying the singular values of the host image with multiple SFs. In the second approach, a hybrid algorithm is proposed based on DWT and SVD.
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