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Nonnegative Tensor Train Factorization with DMRG Technique
Lobachevskii Journal of Mathematics, 2019zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Nonnegative Matrix and Tensor Factorization [Lecture Notes]
IEEE Signal Processing Magazine, 2008In 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.
A. Cichocki, R. Zdunek, S.-i. Amari
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Constrained Nonnegative Tensor Factorization for Clustering
2010 Ninth International Conference on Machine Learning and Applications, 2010Constrained clustering through matrix factorization has been shown to largely improve clustering accuracy by incorporating prior knowledge into the factorization process. Although it has been well studied, none of them deal with constrained multi-way data factorization. Multi-way data or Tensors are encoded as high-order data structures.
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Nonnegative Tensor Cofactorization and Its Unified Solution
IEEE Transactions on Image Processing, 2014In 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 +5 more
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Nonnegative Tensor Factorization with Smoothness Constraints
2008Nonnegative 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 Tensor Train Factorizations and Some Applications
2020Nowadays 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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Algorithms for Nonnegative Tensor Factorization
2009Nonnegative 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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Supervised Nonnegative Tensor Factorization with Maximum-Margin Constraint
Proceedings of the AAAI Conference on Artificial Intelligence, 2013Non-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
Fei Wu +5 more
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Nonnegative tensor factorizations using an alternating direction method
Frontiers of Mathematics in China, 2013zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Cai, Xingju, Chen, Yannan, Han, Deren
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Nonnegative Matrix and Tensor Factorizations : An algorithmic perspective
IEEE Signal Processing Magazine, 2014A 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 +3 more
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