Results 1 - 10 of 94

Nonnegative Tucker decomposition, in

by Yong-deok Kim, Seungjin Choi - Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR-2007 , 2007
"... Nonnegative tensor factorization (NTF) is a recent multiway (multilinear) extension of nonnegative matrix factorization (NMF), where nonnegativity constraints are imposed on the CANDECOMP/PARAFAC model. In this paper we consider the Tucker model with nonnegativity constraints and develop a new tenso ..."
Abstract - Cited by 28 (2 self) - Add to MetaCart
tensor factorization method, referred to as nonnegative Tucker decomposition (NTD). The main contributions of this paper include: (1) multiplicative updating algorithms for NTD; (2) an initialization method for speeding up convergence; (3) a sparseness control method in tensor factorization. Through

Nonnegative Tucker Decomposition with Alpha Divergence

by Yong-deok Kim, Andrzej Cichocki, Seungjin Choi - in Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP2008 , 2008
"... Nonnegative Tucker decomposition (NTD) is a recent multiway extension of nonnegative matrix factorization (NMF), where nonnegativity constraints are incorporated into Tucker model. In this paper we consider α-divergence as a discrepancy measure and derive multiplicative updating algorithms for NTD. ..."
Abstract - Cited by 15 (6 self) - Add to MetaCart
Nonnegative Tucker decomposition (NTD) is a recent multiway extension of nonnegative matrix factorization (NMF), where nonnegativity constraints are incorporated into Tucker model. In this paper we consider α-divergence as a discrepancy measure and derive multiplicative updating algorithms for NTD

QUALITY ASSESSMENT FOR COLOR IMAGES WITH TUCKER DECOMPOSITION

by Cheng Cheng, Hanli Wang
"... As an extension of the singular value decomposition based ap-proaches, a novel metric based on Tucker decomposition for color image quality assessment is proposed in this paper. It extracts both the spacial and chromatic information of a color image with Tucker decomposition. As compared to most of ..."
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As an extension of the singular value decomposition based ap-proaches, a novel metric based on Tucker decomposition for color image quality assessment is proposed in this paper. It extracts both the spacial and chromatic information of a color image with Tucker decomposition. As compared to most

ON TENSOR TUCKER DECOMPOSITION: THE CASE FOR AN ADJUSTABLE CORE SIZE

by Bilian Chen, Zhening Li, Shuzhong Zhang
"... Abstract. This paper is concerned with the problem of finding a Tucker decomposition for tensors. Traditionally, solution methods for Tucker decomposition presume that the size of the core tensor is specified in advance, which may not be a realistic assumption in some applications. In this paper we ..."
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Abstract. This paper is concerned with the problem of finding a Tucker decomposition for tensors. Traditionally, solution methods for Tucker decomposition presume that the size of the core tensor is specified in advance, which may not be a realistic assumption in some applications. In this paper we

1Efficient Nonnegative Tucker Decompositions: Algorithms and Uniqueness

by Guoxu Zhou, Andrzej Cichocki Fellow, Qibin Zhao, Shengli Xie, Senior Member
"... Abstract—Nonnegative Tucker Decomposition (NTD) is a pow-erful tool to extract nonnegative parts-based and physically meaningful latent components from high-dimensional tensor data, while providing natural multiway representations. However, as the data tensor often has multiple modes and is large-sc ..."
Abstract - Cited by 1 (1 self) - Add to MetaCart
Abstract—Nonnegative Tucker Decomposition (NTD) is a pow-erful tool to extract nonnegative parts-based and physically meaningful latent components from high-dimensional tensor data, while providing natural multiway representations. However, as the data tensor often has multiple modes and is large

Discovering Facts with Boolean Tensor Tucker Decomposition

by Dóra Erdős, Pauli Miettinen
"... Open Information Extraction (Open IE) has gained increasing research interest in recent years. The first step in Open IE is to extract raw subject–predicate–object triples from the data. These raw triples are rarely usable per se, and need additional post-processing. To that end, we proposed the use ..."
Abstract - Cited by 2 (2 self) - Add to MetaCart
the use of Boolean Tucker tensor decomposition to simultaneously find the entity and relation synonyms and the facts connecting them from the raw triples. Our method represents the synonym sets and facts using (sparse) binary matrices and tensor that can be efficiently stored and manipulated. We consider

Equivariant and scale-free Tucker decomposition models

by Peter David Hoff , 2013
"... Analyses of array-valued datasets often involve reduced-rank array approximations, typically ob-tained via least-squares or truncations of array decompositions. However, least-squares approxi-mations tend to be noisy in high-dimensional settings, and may not be appropriate for arrays that include di ..."
Abstract - Cited by 4 (1 self) - Add to MetaCart
Analyses of array-valued datasets often involve reduced-rank array approximations, typically ob-tained via least-squares or truncations of array decompositions. However, least-squares approxi-mations tend to be noisy in high-dimensional settings, and may not be appropriate for arrays that include

TENSOR DICTIONARY LEARNINGWITH SPARSE TUCKER DECOMPOSITION

by Syed Zubair, Wenwu Wang
"... Dictionary learning algorithms are typically derived for deal-ing with one or two dimensional signals using vector-matrix operations. Little attention has been paid to the problem of dictionary learning over high dimensional tensor data. We propose a new algorithm for dictionary learning based on te ..."
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on ten-sor factorization using a TUCKER model. In this algorithm, sparseness constraints are applied to the core tensor, of which the n-mode factors are learned from the input data in an al-ternate minimization manner using gradient descent. Simu-lations are provided to show the convergence and the recon

Equivariant and scale-free Tucker decomposition models

by Peter David , 2013
"... Analyses of array-valued datasets often involve reduced-rank array approximations, typically obtained via least-squares or truncations of array decompositions. However, least-squares approximations tend to be noisy in high-dimensional settings, and may not be appropriate for arrays that include disc ..."
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Analyses of array-valued datasets often involve reduced-rank array approximations, typically obtained via least-squares or truncations of array decompositions. However, least-squares approximations tend to be noisy in high-dimensional settings, and may not be appropriate for arrays that include

Infinite Tucker Decomposition: Nonparametric Bayesian Models for Multiway Data Analysis

by Zenglin Xu, Feng Yan, Yuan Qi - In Proceedings of the International Conference on Machine Learning (ICML , 2012
"... Tensor decomposition is a powerful computational tool for multiway data analysis. Many popular tensor decomposition approaches—such as the Tucker decomposition and CANDE-COMP/PARAFAC (CP)—amount to multi-linear factorization. They are insufficient to model (i) complex interactions between data entit ..."
Abstract - Cited by 14 (2 self) - Add to MetaCart
Tensor decomposition is a powerful computational tool for multiway data analysis. Many popular tensor decomposition approaches—such as the Tucker decomposition and CANDE-COMP/PARAFAC (CP)—amount to multi-linear factorization. They are insufficient to model (i) complex interactions between data
Results 1 - 10 of 94