Sparse Representation for Signal Classification

By Ke Huang, Selin Aviyente. Neural Information Processing Systems, 2006

701 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computer Vision and Pattern Recognition, Signal Processing

View Paper: https://www.semanticscholar.org/paper/63140301f88a0c5223f92afbf2acfec9e537f6be

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Mechanics

In Computer Vision and Pattern Recognition

In Signal Processing

Citations in the corpus

Ensemble Sparse Classification of Alzheimer’s Disease (2012, background): “…representation technique, which can be regarded as one of the recent major achievements in pattern classification, has been proposed and successfully used for various classification problems, e.g., robust face recognition (Huang and Aviyente, 2007; Majumdar and Ward, 2009; Wright et al., 2009).”
Sparse Representation Based Fisher Discrimination Dictionary Learning for Image Classification (2014, background): “…in face recognition (Wright et al. 2009b; Wang et al. 2009; Yang and Zhang 2010; Yang et al. 2011a,b), handwritten digit and texture classification (Huang and Aviyente 2006; Mairal et al. 2009; Ramirez et al. 2010; Yang et al. 2010b; Rodriguez and Sapiro 2007; Mairal et al. 2012), natural image…”
Sparse Multilayer Perceptron for Phoneme Recognition (2012, methodology): “Sparse features have been successfully used in many machine learning applications such as face recognition [8], handwritten digit recognition [6], [7], [9], and phoneme recognition [10], [12], [13].”
Transfer Sparse Coding for Robust Image Representation (2013, background): “interest in machine learning, pattern recognition, signal processing [9, 11, 8], and has been successfully applied to image classification [22, 12, 24] and face recognition [21, 6].”
Discriminative learned dictionaries for local image analysis (2008, methodology): “Instead of this reconstruction-based approach, we show that better results can be achieved, in general, by learning discriminative sparse representations, while keeping the same robustness against noise or occlusions (see also [9]), which discriminative methods may be sensitive to [40].”
Fisher Discrimination Dictionary Learning for sparse representation (2011, methodology): “Recently sparse representation techniques have also led to promising results in image classification, e.g. face recognition (FR) [5] [6] [7] [10] [31], digit and texture classification [8] [9] [11] [12], etc.”
Supervised Dictionary Learning (2008, methodology): “Compared with earlier work using one dictionary per class [6], our model has the advantage of letting multiple classes share some features, and uses the coefficients α of the sparse representations as part of the classification procedure, thereby following the works from [3, 4, 5], but with learned ”
Sparse representation or collaborative representation: Which helps face recognition? (2011, background): “Huang et al. [7] sparsely coded a signal over a set of redundant bases and classified the signal based on its coding vector.”
Structured sparsity through convex optimization (2011, neutral): “• “Reconstructive” training { minD− ∑ i∈S− R(xi,D−) minD+ ∑ i∈S+ R(xi,D+) (Grosse et al., 2007; Huang and Aviyente, 2006; Sprechmann et al., 2010)”
Robust Recovery of Subspace Structures by Low-Rank Representation (2010, background): “Subspace segmentation is an important data clustering problem as it arises in numerous research areas, including computer vision [3], [4], [11], [12], image processing [5], [13], [14], machine learning [15], [16] and system identification [17].”
Robust Face Recognition via Sparse Representation (2009, background): “A related, but simpler, measure of parsimony in high-dimensional data processing seeks models that depend on only a few of the observations, selecting a small subset of features for classiﬁcation or visualization (e.g., Sparse PCA [3], [4] amongst others).”
Classification and clustering via dictionary learning with structured incoherence and shared features (2010, background): “(C) is the best result obtained in [12] (only USPS available).”
Sparse reconstruction cost for abnormal event detection (2011, methodology): “The key part of our algorithm is the sparsity pursuit , which has been a hot topic in machine learning recently and includes cardinality sparsity [11], group sparsity [28], matrix or tensor rank sparsity [17].”
Towards Scalable Summarization of Consumer Videos Via Sparse Dictionary Selection (2012, background): “The group Lasso is a kind of sparse pursuit technology, which includes cardinality sparsity [30] and tensor rank sparsity [31] as well.”
Deep Spatial Feature Reconstruction for Partial Person Re-identification: Alignment-free Approach (2018, methodology): “SRC has been also applied to signal classification [8], visual tracking [15], and visual classification [26], etc.”
Sparse Modeling for Image and Vision Processing (2014, methodology): “independent component analysis formulation of Hyvärinen et al. [2001]. The principle is similar to the hierarchical case—that is, we can use a penalty Ψ as in (2.”
Abnormal event detection in crowded scenes using sparse representation (2013, background): “In fact, the SRC function also can be equivalently mapped to the framework of Bayesian decision like in [13].”