Results 161 to 170 of about 36,142 (194)

Nonnegative Tensor Cofactorization and Its Unified Solution

IEEE Transactions on Image Processing, 2014
In this paper, we present a new joint factorization algorithm, called Nonnegative Tensor Co-Factorization (NTCoF). The key idea is to simultaneously factorize multiple visual features of the same data into nonnegative dimensionality-reduced representations, and meanwhile, to maximize the correlations of the low-dimensional representations.
Liu, Xiaobai, Xu, Qian, Yan, Shuicheng, Wang, Gang, Jin, Hai, Lee, Seong-Whan   +5 more
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Nonnegative Tensor Decomposition

2013
It is more and more common to encounter applications where the collected data is most naturally stored or represented in a multi-dimensional array, known as a tensor. The goal is often to approximate this tensor as a sum of some type of combination of basic elements, where the notation of what is a basic element is specific to the type of factorization
N. Hao, L. Horesh, M. E. Kilmer
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Nonnegative contraction/averaging tensor factorization

10th Symposium on Neural Network Applications in Electrical Engineering, 2010
Nonnegative tensor factorization (NTF) is a recent multiway (multilinear) extension of negative matrix factorization (NMF), where nonnegativity constraints are mainly imposed on CANDECOMP/PARAFAC model and recently, also, on Tucker model. Nonnegative tensor factorization algorithms have many potential applications, including multiway clustering, multi ...
Marko V. Jankovic, Branimir Reljin
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NONNEGATIVE TENSOR FACTORIZATION FOR CONTINUOUS EEG CLASSIFICATION

International Journal of Neural Systems, 2007
In this paper we present a method for continuous EEG classification, where we employ nonnegative tensor factorization (NTF) to determine discriminative spectral features and use the Viterbi algorithm to continuously classify multiple mental tasks. This is an extension of our previous work on the use of nonnegative matrix factorization (NMF) for EEG ...
Lee, H, Kim, YD, Cichocki, A, Choi, S
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Singular values of nonnegative rectangular tensors

Frontiers of Mathematics in China, 2011
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Yang, Yuning, Yang, Qingzhi
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Spectral unmixing using nonnegative tensor factorization

Proceedings of the 45th annual southeast regional conference, 2007
Three major objectives in processing hyperspectral image data of an object (target) are data compression, spectral signature identification of constituent materials, and determination of their corresponding fractional abundances. Here we propose a novel approach to processing hyperspectral data using nonnegative tensor factorization (NTF), which ...
Qiang Zhang, Han Wang, Robert Plemmons, V. Paul Pauca   +3 more
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Sparseness constraints on nonnegative tensor decomposition

2010 Conference Record of the Forty Fourth Asilomar Conference on Signals, Systems and Computers, 2010
The Nonnegative Tensor Factorization (NTF) method, has been shown to separate the mixture of several sound sources reasonably well. Based on the sparsity of power spectrogram of signals, we propose to add sparseness constraints to one factor matrix, which contains frequency basis, to obtain a sparse representation of this nonnegative factor.
Na Li, Carmeliza Navasca
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Nonnegative Tensor Factorization Accelerated Using GPGPU

IEEE Transactions on Parallel and Distributed Systems, 2011
This article presents an optimized algorithm for Nonnegative Tensor Factorization (NTF), implemented in the CUDA (Compute Uniform Device Architecture) framework, that runs on contemporary graphics processors and exploits their massive parallelism. The NTF implementation is primarily targeted for analysis of high-dimensional spectral images, including ...
Antikainen, Jukka, Havel, Jiri, Josth, Radovan, Herout, Adam, Zemcik, Pavel, Hauta-Kasari, Markku   +5 more
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Nonnegative Tensor Train Factorization with DMRG Technique

Lobachevskii Journal of Mathematics, 2019
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Algorithms for Nonnegative Tensor Factorization

2009
Nonnegative Matrix Factorization (NMF) is a decomposition which incorporates nonnegativity constraints in both the weights and the bases of the representation. The nonnegativity constraints in NMF correspond better to the intuitive notion of combining parts in order to create a complete object, since the object is represented using only additions of ...
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