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 (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. 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

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

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

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

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

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

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 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