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SinglePass KSVD for Efficient Dictionary Learning
"... Abstract Sparse representation has been widely used in machine learning, signal processing and communications. KSVD, which generalizes kmeans clustering, is one of the most famous algorithms for sparse representation and dictionary learning. KSVD is an iterative method that alternates between en ..."
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encoding the data sparsely by using the current dictionary and updating the dictionary based on the sparsely represented data. In this paper, we introduce a singlepass KSVD method. In this method, the previous input data are first summarized as a condensed representation of weighted samples. Then, we
KSVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation
, 2006
"... In recent years there has been a growing interest in the study of sparse representation of signals. Using an overcomplete dictionary that contains prototype signalatoms, signals are described by sparse linear combinations of these atoms. Applications that use sparse representation are many and inc ..."
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Cited by 930 (41 self)
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signal representations. Given a set of training signals, we seek the dictionary that leads to the best representation for each member in this set, under strict sparsity constraints. We present a new method—the KSVD algorithm—generalizing the umeans clustering process. KSVD is an iterative method
Discriminative KSVD for dictionary learning in face recognition
 In CVPR
"... In a sparserepresentationbased face recognition scheme, the desired dictionary should have good representational power (i.e., being able to span the subspace of all faces) while supporting optimal discrimination of the classes (i.e., different human subjects). We propose a method to learn an over ..."
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Cited by 103 (0 self)
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classifier and the representational power of the dictionary being considered at the same time by the same optimization procedure. The DKSVD algorithm finds the dictionary and solves for the classifier using a procedure derived from the KSVD algorithm, which has proven efficiency and performance
Efficient Implementation of the KSVD Algorithm using Batch Orthogonal Matching Pursuit
"... The KSVD algorithm is a highly effective method of training overcomplete dictionaries for sparse signal representation. In this report we discuss an efficient implementation of this algorithm, which both accelerates it and reduces its memory consumption. The two basic components of our implementati ..."
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Cited by 53 (1 self)
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The KSVD algorithm is a highly effective method of training overcomplete dictionaries for sparse signal representation. In this report we discuss an efficient implementation of this algorithm, which both accelerates it and reduces its memory consumption. The two basic components of our
KSVD DICTIONARYLEARNING FOR THE ANALYSIS SPARSE MODEL
"... The synthesisbased sparse representation model for signals has drawn a considerable interest in the past decade. Such a model assumes that the signal of interest can be decomposed as a linear combination of a few atoms from a given dictionary. In this paper we concentrate on an alternative, analysi ..."
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Cited by 8 (0 self)
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, analysisbased model, where an Analysis Dictionary multiplies the signal, leading to a sparse outcome. Our goal is to learn the analysis dictionary from a set of signal examples, and the approach taken is parallel and similar to the one adopted by the KSVD algorithm that serves the corresponding problem
KSVD: Design of dictionaries for sparse representation
 IN: PROCEEDINGS OF SPARS’05
, 2005
"... In recent years there is a growing interest in the study of sparse representation for signals. Using an overcomplete dictionary that contains prototype signalatoms, signals are described by sparse linear combinations of these atoms. Recent activity in this field concentrated mainly on the study of ..."
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Cited by 45 (1 self)
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of pursuit algorithms that decompose signals with respect to a given dictionary. In this paper we propose a novel algorithm – the KSVD algorithm – generalizing the KMeans clustering process, for adapting dictionaries in order to achieve sparse signal representations. We analyze this algorithm
NewsWeeder: Learning to Filter Netnews
 in Proceedings of the 12th International Machine Learning Conference (ML95
, 1995
"... A significant problem in many information filtering systems is the dependence on the user for the creation and maintenance of a user profile, which describes the user's interests. NewsWeeder is a netnewsfiltering system that addresses this problem by letting the user rate his or her interest l ..."
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Cited by 555 (0 self)
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level for each article being read (15), and then learning a user profile based on these ratings. This paper describes how NewsWeeder accomplishes this task, and examines the alternative learning methods used. The results show that a learning algorithm based on the Minimum Description Length (MDL
SemiSupervised Learning Literature Survey
, 2006
"... We review the literature on semisupervised learning, which is an area in machine learning and more generally, artificial intelligence. There has been a whole
spectrum of interesting ideas on how to learn from both labeled and unlabeled data, i.e. semisupervised learning. This document is a chapter ..."
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Cited by 757 (8 self)
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We review the literature on semisupervised learning, which is an area in machine learning and more generally, artificial intelligence. There has been a whole
spectrum of interesting ideas on how to learn from both labeled and unlabeled data, i.e. semisupervised learning. This document is a
Machine Learning in Automated Text Categorization
 ACM COMPUTING SURVEYS
, 2002
"... The automated categorization (or classification) of texts into predefined categories has witnessed a booming interest in the last ten years, due to the increased availability of documents in digital form and the ensuing need to organize them. In the research community the dominant approach to this p ..."
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Cited by 1658 (22 self)
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to this problem is based on machine learning techniques: a general inductive process automatically builds a classifier by learning, from a set of preclassified documents, the characteristics of the categories. The advantages of this approach over the knowledge engineering approach (consisting in the manual
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