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482
KSVD and its nonnegative variant for dictionary design
 Proceedings of the SPIE conference wavelets
, 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 as sparse linear combinations of these atoms. Recent activity in this field concentrated mainly on the study of ..."
Modeling Receptive Fields with NonNegative Sparse Coding
, 2003
"... An important approach in visual neuroscience considers how the processing of the early visual system is dependent on the statistics of the natural environment. A particularly influential model in this respect has been sparse coding. In this paper we argue for a nonnegative variant of the model. Thi ..."
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Cited by 24 (3 self)
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An important approach in visual neuroscience considers how the processing of the early visual system is dependent on the statistics of the natural environment. A particularly influential model in this respect has been sparse coding. In this paper we argue for a nonnegative variant of the model
NonNegative Graph Embedding
"... We introduce a general formulation, called nonnegative graph embedding, for nonnegative data decomposition by integrating the characteristics of both intrinsic and penalty graphs [17]. In the past, such a decomposition was obtained mostly in an unsupervised manner, such as Nonnegative Matrix Facto ..."
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Cited by 9 (2 self)
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Factorization (NMF) and its variants, and hence unnecessary to be powerful at classification. In this work, the nonnegative data decomposition is studied in a unified way applicable for both unsupervised and supervised/semisupervised configurations. The ultimate data decomposition is separated into two parts
Variants of NonNegative LeastMeanSquare Algorithm and Convergence Analysis
"... Abstract—Due to the inherent physical characteristics of systems under investigation, nonnegativity is one of the most interesting constraints that can usually be imposed on the parameters to estimate. The NonNegative LeastMeanSquare algorithm (NNLMS) was proposed to adaptively find solutions o ..."
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Cited by 2 (1 self)
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Abstract—Due to the inherent physical characteristics of systems under investigation, nonnegativity is one of the most interesting constraints that can usually be imposed on the parameters to estimate. The NonNegative LeastMeanSquare algorithm (NNLMS) was proposed to adaptively find solutions
1Variants of nonnegative leastmeansquare algorithm and convergence analysis
"... Abstract—Due to the inherent physical characteristics of systems under investigation, nonnegativity is one of the most interesting constraints that can usually be imposed on the parameters to estimate. The NonNegative LeastMeanSquare algorithm (NNLMS) was proposed to adaptively find solutions o ..."
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Abstract—Due to the inherent physical characteristics of systems under investigation, nonnegativity is one of the most interesting constraints that can usually be imposed on the parameters to estimate. The NonNegative LeastMeanSquare algorithm (NNLMS) was proposed to adaptively find solutions
Note on the fundamental theorem on irreducible nonnegative matrices
 Proceedings of the Edinburgh Mathematical Society, 11:127130, 1958/1959. Journal of Linear Algebra ISSN 10813810 A publication of the International Linear Algebra Society Volume
"... 1. Let A = [aii] be an nth order irreducible nonnegative matrix. As is very wellknown, the matrix A has a positive characteristic root p (provided that n> I), which is simple and maximal in the sense that every characteristic root A satisfies I A I ~ p, and the characteristic vector x belongin ..."
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Cited by 3 (0 self)
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belonging to p may be chosen positive. These results, originally due to Frobenius, have been proved by Wielandt (4) by means of a strikingly simple basic idea. Recently, a variant of Wielandt's proof has been given by Householder (2). We shall sketch part of the proof. For each nonnegative column
Conformally flat manifolds with non–negative Ricci curvature
 Compositio Math
, 2007
"... Abstract. We show that complete conformally flat manifolds of dimension n � 3 with nonnegative Ricci curvature enjoy nice rigidity properties: they are either flat, or locally isometric to a product of a sphere and a line, or are globally conformally equivalent to R n or to a spherical spaceform S n ..."
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Cited by 9 (0 self)
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complete noncompact conformally flat manifolds with nonnegative Ricci curvature and fast curvature decay at infinity are the complete flat manifolds. One motivation for this result is that it stands as an analog on real manifolds of wellknown rigidity and gap phenomena on Kähler manifolds with
Nonnegative Matrix Factorizations for Clustering: A Survey
"... Recently there has been significant development in the use of nonnegative matrix factorization (NMF) methods for various clustering tasks. NMF factorizes an input nonnegative matrix into two nonnegative matrices of lower rank. Although NMF can be used for conventional data analysis, the recent over ..."
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Cited by 1 (0 self)
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comprehensive review of nonnegative matrix factorization methods for clustering. In particular, we outline the theoretical foundations on NMF for clustering, provide an overview of different variants on NMF formulations, and examine
Nonstationary analysis of the convergence of the nonnegative leastmeansquare algorithm
 in Proc. EUSIPCO
, 2013
"... Nonnegativity is a widely used constraint in parameter estimation procedures due to physical characteristics of systems under investigation. In this paper, we consider an LMStype algorithm for system identification subject to nonnegativity constraints, called NonNegative LeastMeanSquare algo ..."
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Cited by 2 (1 self)
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Nonnegativity is a widely used constraint in parameter estimation procedures due to physical characteristics of systems under investigation. In this paper, we consider an LMStype algorithm for system identification subject to nonnegativity constraints, called NonNegative Least
Results 1  10
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482