K-SVD and its non-negative variant for dictionary design

By Michal Aharon, Michael Elad, Alfred Marcel Bruckstein. 2005

182 citations recorded by the source corpus.

Subfields: Computational Mechanics, Mechanics of Materials, Speech & Audio, Signal Processing

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Citations in the corpus

Pedestrian Detection with Unsupervised Multi-stage Feature Learning (2012, background): “Many efficient algorithms for solving the above convex system has been proposed in recent years [1, 6, 2, 26].”
Fast Inference in Sparse Coding Algorithms with Applications to Object Recognition (2010, background): “Many sparse coding algorithms have been shown to learn good local feature extra ctors for natural images [4, 5, 6, 7, 8].”
Learning invariant features through topographic filter maps (2009, background): “…proposed in the literature and in this work we focus on the following convex formulation: This particular formulation has been extensively studied [19, 4, 2, 14, 17, 24, 16, 9], and it has also been extended to the case when the dictionary D is learned, thus adapting to the statistics of the input.”
What is the best multi-stage architecture for object recognition? (2009, methodology): “A system with two such stages trained in unsupervised mode, and the classifier (only) trained in supervised mode, as in [25], is denoted UU.”
From Sparse Solutions of Systems of Equations to Sparse Modeling of Signals and Images (2009, methodology): “The matrix factorization viewpoint connects this problem with related problems of nonnegative matrix factorization [104, 55] and sparse nonnegative matrix factorization [92, 1].”
Compressive light field photography using overcomplete dictionaries and optimized projections (2013, methodology): “The nonnegative variant of K-SVD [Aharon et al. 2005] (K-SVD NN) allows only positive values in the dictionary atoms as well as in the coefficients, i.”
Learning Convolutional Feature Hierarchies for Visual Recognition (2010, background): “One can use either batch [7] (by accumu lating derivatives over many samples) or online updates [8, 6, 5] (updating the dicti onary after each sample).”