Results 81 to 90 of about 130 (119)

Nonnegative Tensor Factorization with Smoothness Constraints

2008
Nonnegative Tensor Factorization (NTF) is an emerging technique in multidimensional signal analysis and it can be used to find parts-based representations of high-dimensional data. In many applications such as multichannel spectrogram processing or multiarray spectra analysis, the unknown features have locally smooth temporal or spatial structure.
Rafal Zdunek, Tomasz M. Rutkowski
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Nonnegative Matrix and Tensor Factorization [Lecture Notes]

IEEE Signal Processing Magazine, 2008
In these lecture notes, the authors have outlined several approaches to solve a NMF/NTF problem. The following main conclusions can be drawn: 1) Multiplicative algorithms are not necessary the best approaches for NMF, especially if data representations are not very redundant or sparse.
Andrzej Cichocki, Rafal Zdunek, Shun-ichi Amari   +2 more
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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 J. Plemmons, V. Paul Pauca   +3 more
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Nonnegative Matrix and Tensor Factorizations : An algorithmic perspective

IEEE Signal Processing Magazine, 2014
A common thread in various approaches for model reduction, clustering, feature extraction, classification, and blind source separation (BSS) is to represent the original data by a lower-dimensional approximation obtained via matrix or tensor (multiway array) factorizations or decompositions.
Guoxu Zhou, Andrzej Cichocki, Qibin Zhao, Shengli Xie   +3 more
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Nonnegative Tensor Train Factorizations and Some Applications

2020
Nowadays as the amount of available data grows, the problem of managing information becomes more difficult. In many applications data can be represented as a multidimensional array. However, in the big data case and as well as when we aim at discovering some structure in the data, we are often interested to construct some low-rank tensor approximations,
Elena Shcherbakova, Eugene Tyrtyshnikov
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XRAY Algorithm for Separable Nonnegative Tensor Factorization

2017
Many computational problems in machine learning can be represented by separable matrix factorization models. In a geometric approach, linear separability means that the whole set of data points can be modeled by a convex combination of a few data points, referred to as the extreme rays.
Rafal Zdunek, Tomasz Sadowski
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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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Supervised Nonnegative Tensor Factorization with Maximum-Margin Constraint

Proceedings of the AAAI Conference on Artificial Intelligence, 2013
Non-negative tensor factorization (NTF) has attracted great attention in the machine learning community. In this paper, we extend traditional non-negative tensor factorization into a supervised discriminative decomposition, referred as Supervised Non-negative Tensor Factorization with Maximum-Margin Constraint(SNTFM2). SNTFM2 formulates
Wu, Fei, Tan, Xu, Yang, Yi, Tao, Dacheng, Tang, Siliang, Zhuang, Yueting   +5 more
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Numerical method for the generalized nonnegative tensor factorization problem

Numerical Algorithms, 2020
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Xue-Feng Duan, Juan Li, Shan-Qi Duan, Qing-Wen Wang 0001   +3 more
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Action Recognition in Videos Using Nonnegative Tensor Factorization

2010 20th International Conference on Pattern Recognition, 2010
Recognizing human actions is of vital interest in video surveillance or ambient assisted living. We consider an action as a sequence of body poses which are themselves a linear combination of body parts. In an offline procedure, nonnegative tensor factorization is used to extract basis images that represent body parts.
Barbara Krausz 0001, Christian Bauckhage
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