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19 Mar 2014 The fundamental idea behind the algorithm is to learn a sparse representation in two phases. In the first phase, the whole training dataset is partitioned into small non-overlapping subsets, and a dictionary is trained independently on each small database. In the second phase, the dictionaries are merged to
ABSTRACT | Sparse and redundant representation modeling of data assumes an KEYWORDS | Dictionary learning; harmonic analysis; signal approximation; signal representation; sparse coding; sparse representation. I. INTRODUCTION. The process of merging the advantages of trained and analytic dictionaries,.
self-explanatory sparse representation (MSSR) is proposed to capture and combine various salient regions and structures from different kernel spaces. This is equivalent to learning a nonlinear structured dictionary, whose complexity is reduced by learning a set of smaller dictionary blocks via SSSR. SSSR and MSSR are
A Split-and-Merge Dictionary Learning Algorithm for Sparse Representation: Application to Image. Denoising. Subhadip Mukherjee. Department of Electrical Engineering. Indian Institute of Science. Bangalore 560012, India. Email: subhadip@ee.iisc.ernet.in. Chandra Sekhar Seelamantula. Department of Electrical
the applications of the sparse representation for different tasks, such as signal separation, denoising, coding, image inpainting [6, 7, 8, 9, 10]. For instance, in [6], sparse representation is used for image separation. The overcomplete dictionary is generated by combining multiple standard transforms, including curvelet
19 Mar 2014 Full-Text Paper (PDF) | Mar 19, 2014 | In big data image/video analytics, we encounter the problem of learning an overcomplete dictionary for sparse representation from a large training dataset, which can not be processed at once because of storage and computational constraints. To tackle the
Finding a sparse representation (based on the use of a “few" code or dictionary words) can also be viewed as a generalization of vector quantization where a . is obtained from the generative signal model, equation 1.3, by assuming that x has the parameterized (generally nongaussian) probability density function (pdf),.
Efficient sparse representation based classification using hierarchically structured dictionaries Recently, it has been proposed to use sparse representation based classification (SRC) [1] for automatic speech recognition The overall size of the dictionary is kept down by merging atoms that have a decreasing weight. II.
tation. For example, Yuan and Yan [25] proposed a multi-task sparse linear regression model for image classification. This method uses group sparsity to combine different features of an object for classification. Zhang et al. [26] proposed a joint dynamic sparse representation model for object recognition. Their essential goal
24 Mar 2017 learning, face recognition, sparse representation based classifi- cation, single labeled sample per person. Recently, the Sparse Representation based Classification. The authors would like to thank Weichao Qiu and Mingbo Furthermore, by combining with more recent RADL method, S3RC-RADL
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