Titlebook: Sparse Representation, Modeling and Learning in Visual Recognition; Theory, Algorithms a Hong Cheng Book 2015 Springer-Verlag London 2015 C

Accolade

Sparse Recovery Approaches the sparse signal which includes the linear programming method, second-order cone programming method, -homotopy method, and elastic net. Second, it outlines some classic greedy algorithms such as, MP, OMP, CoSaMP, and iterated hard thresholding methods. Third, it outlines the sparse signal recovery

察觉

Robust Sparse Representation, Modeling and Learningonnection between MLE and residuals is given before giving the introduction to M-estimator, which help in better understanding the M-estimator and robust regression. Then, it describes robust sparse PCA which can be efficiently used in feature learning. Lastly, it gives some applications about the r

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Traumatic-Grief

Feature Representation and Learningare detailed. Then it gives the concepts of dictionary learning methods including K-SVD, discriminative dictionary learning, online dictionary learning, supervised dictionary learning, and joint dictionary leaning and some applications about dictionary learning. Lastly, it includes some works about

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Sparsity-Induced Similarityarse-induced similarity (SIS), and it also uses a toy problem to illustrate it intuitively. Then, it gives some extensions as nonnegative sparsity-induced similarity. Third, some basic issues of SIS are presented. Lastly, it includes some applications of SIS including label propagation, human activi

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Sparse Representation and Learning-Based Classifierslassifier (SRC) which is very useful in human face recognition. Then, it includes spatial pyramid matching sparse coding method which replaces the vector quantization method and it is widely used in scene recognition. Third, it describes the sparsity-based nearest neighbor classifiers and sparse cod