Index Terms— Dictionary Learning
K-WEB: Nonnegative dictionary learning for sparse image representations
... Index Terms— Dictionary learning, sparse representa- tions, K-SVD, NMF ...INTRODUCTION Dictionary learning methods aim at finding a suitable repre- sentation of the data, namely ...
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Dictionary Learning for a Sparse Appearance Model in Visual Tracking
... Index Terms— dictionary learning, sparse coding, parti- cle filtering, object tracking ...The dictionary is com- posed of two parts: target templates used to represent the entire object ...
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Tissue motion estimation using dictionary learning: application to cardiac amyloidosis
... Index Terms—Cardiac motion estimation, dictionary learn- ing, cardiac amyloidosis. I. INTRODUCTION The amyloidoses are a rare group of diseases that re- sult from extracellular deposition in organs ...
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Learning an Adaptive Dictionary Structure for Efficient Image Sparse Coding
... DCT dictionary is clearly below the learned dictionaries in terms of quality of ...the learning of a branch in the tree is stopped after an incomplete dictionary, many branches cannot reach 10 ...
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Compressed Online Dictionary Learning for Fast Resting-State fMRI Decomposition
... Index Terms— resting-state fMRI, sparse decomposition, dic- tionary learning, online learning, range-finder ...statistical learning, ...
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Indian Buffet process dictionary learning for image inpainting
... Index Terms— sparse representations, dictionary learn- ing, inverse problems, Indian Buffet Process ...by dictionary learning (DL) from a set of refer- ence ...penalty terms on ...
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Electro-Metabolic Coupling Investigated with Jitter Invariant Dictionary Learning
... Invariant Dictionary Learning Sebastian Hitziger 1 , Maureen Clerc 1 , Alexandre Gramfort 2 , Sandrine Saillet 3 , Christian Bénar 4 , Théodore Papadopoulo 1 1 Project-Team Athena, Inria Sophia Antipolis, ...
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Sample Complexity of Dictionary Learning and other Matrix Factorizations
... of Dictionary Learning and other Matrix Factorizations R´emi Gribonval, IEEE Fellow, Rodolphe Jenatton, Francis Bach, Martin Kleinsteuber, Matthias Seibert Abstract—Many modern tools in machine ...
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Online Graph Dictionary Learning
... Representation Learning Processing of graph data in machine learning applications have traditionally been handled using implicit representations such as with graph kernels ( Shervashidze et ...deep ...
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Dictionary Digitization as a Lexicographic Challenge. Technical Terms in the Lexicon Mediae et Infimae Latinitatis Polonorum
... specific (and useful) geom. (45 occurrences) or astr., since it is first and foremost optical and astronomical texts that supply mathematical content in the LMILP. In what concerns label usage proportions the following ...
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Sparse and spurious: dictionary learning with noise and outliers
... spurious: dictionary learning with noise and outliers R´emi Gribonval, IEEE Fellow, Rodolphe Jenatton, Francis Bach Abstract—A popular approach within the signal processing and machine learning ...
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Learning a common dictionary over a sensor network
... the dictionary and the sparsity of the coefficients are ...the dictionary contains each one of the true data underlying the noisy observation so that only one non zero coefficient in x(i) would be ...
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MAXIMUM MARGINAL LIKELIHOOD ESTIMATION FOR NONNEGATIVE DICTIONARY LEARNING
... hood, i.e., the likelihood of the dictionary where the expansion coef- ficients have been integrated out (given a Gamma conjugate prior). We compare the output of both maximum joint likelihood estima- tion (i.e., ...
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Dictionary Learning for Pattern Classification in Medical Imaging
... The general principle of sparsity or parsimony is to represent some phe- nomenon using as few variables as possible. The notion of parsimony is in- spired from Ockham's razor, a principle stated by the philosopher ...
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Spatially Constrained Online Dictionary Learning for Source Separation
... We propose a dictionary learning method that introduces sparsity constraints on the spatial localisation of sources from external knowledge. Additional constraints on the mixing matrix (positivity and ...
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Classification of Multiple Sclerosis Lesions using Adaptive Dictionary Learning
... and dictionary learning techniques offer a great advantage in medical image ...the dictionary learning framework has been used in deformable segmentation [13], image fusion [14], ...
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Dictionary learning based sinogram inpainting for CT sparse reconstruction
... based dictionary learning (DL) in signal processing ...utilized dictionary learning based method to denoise the sinogram in low-dose ...
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Adaptive Dictionary Learning For Competitive Classification Of Multiple Sclerosis Lesions
... over-complete dictionary and have been used in many image processing ...adaptive dictionary learning paradigm to automatically classify Multiple Sclerosis (MS) lesions from ...of learning ...
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Jitter-Adaptive Dictionary Learning -Application to Multi-Trial Neuroelectric Signals
... Denoising the signals: For PCA, denoising was performed by setting the coefficients of all but the first K components to zero. For DL and JADL, the noisy signals were encoded over the learned dictionaries according to ...
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Multivariate dictionary learning and shift & 2D rotation invariant sparse coding
... 4.1. Multivariate OMP Sparse approximation can be achieved by any algorithm able to overcome the high coherence due to the shift-invariant case. OMP is chosen for its speed : a more precise description is given in [6]. ...
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