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Nonnegative Tucker Decomposition
2007 IEEE Conference on Computer Vision and Pattern Recognition, 2007Nonnegative 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 tensor factorization method, referred to as nonnegative ...
Yong-Deok Kim, Seungjin Choi
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Compression of hyperspectral images based on Tucker decomposition and CP decomposition
Journal of the Optical Society of America A, 2022Hyperspectral imagers are developing towards high resolution, high detection sensitivity, broad spectra, and wide coverage, which means that hyperspectral data are getting more and more substantial. This brings a great challenge to data storage and real-time transmission of hyperspectral data.
Lei, Yang +7 more
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D-Tucker: Fast and Memory-Efficient Tucker Decomposition for Dense Tensors
2020 IEEE 36th International Conference on Data Engineering (ICDE), 2020Given a dense tensor, how can we find latent patterns and relations efficientlyƒ Existing Tucker decomposition methods based on Alternating Least Square (ALS) have limitations in terms of time and space since they directly handle large dense tensors to obtain the result of Tucker decomposition.
U Kang, Jun-Gi Jang
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Algorithms for Sparse Nonnegative Tucker Decompositions
Neural Computation, 2008There is a increasing interest in analysis of large-scale multiway data. The concept of multiway data refers to arrays of data with more than two dimensions, that is, taking the form of tensors. To analyze such data, decomposition techniques are widely used.
Morten Mørup +2 more
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Nonnegative Tucker decomposition with alpha-divergence
2008 IEEE International Conference on Acoustics, Speech and Signal Processing, 2008Nonnegative tucker decomposition (NTD) is a recent multiway extension of nonnegative matrix factorization (NMF), where nonnega- tivity constraints are incorporated into Tucker model. In this paper we consider alpha-divergence as a discrepancy measure and derive multiplicative updating algorithms for NTD.
Yong-Deok Kim +2 more
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On Fast algorithms for orthogonal Tucker decomposition
2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2014We propose algorithms for Tucker tensor decomposition, which can avoid computing singular value decomposition or eigenvalue decomposition of large matrices as in the work-horse higher order orthogonal iteration (HOOI) algorithm. The novel algorithms require computational cost of O(I 3 R), which is cheaper than O(I 3 R + IR 4 + R 6 ) of HOOI for ...
Anh Huy Phan 0001 +2 more
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Parallel Tucker Decomposition with Numerically Accurate SVD
50th International Conference on Parallel Processing, 2021Tucker decomposition is a low-rank tensor approximation that generalizes a truncated matrix singular value decomposition (SVD). Existing parallel software has shown that Tucker decomposition is particularly effective at compressing terabyte-sized multidimensional scientific simulation datasets, computing reduced representations that satisfy a specified
Zitong Li, Qiming Fang, Grey Ballard
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On optimizing distributed non-negative Tucker decomposition
Proceedings of the ACM International Conference on Supercomputing, 2019The Tucker decomposition generalizes singular value decomposition (SVD) to high dimensional tensors. It factorizes a given N-dimensional tensor as the product of a small core tensor and a set of N factor matrices. Non-negative Tucker Decomposition (NTD) is a variant that imposes the constraint that the entries of the core and the factor matrices must ...
Venkatesan T. Chakaravarthy +3 more
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An Efficient Randomized Algorithm for Computing the Approximate Tucker Decomposition
Journal of Scientific Computing, 2021zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Maolin Che +2 more
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Fast and Efficient Algorithms for Nonnegative Tucker Decomposition
Lecture Notes in Computer Science, 2008In this paper, we propose new and efficient algorithms for nonnegative Tucker decomposition (NTD): Fast i¾?-NTD algorithm which is much precise and faster than i¾?-NTD [1]; and β-NTD algorithm based on the βdivergence. These new algorithms include efficient normalization and initialization steps which help to reduce considerably the running time and ...
Anh-Huy Phan, Andrzej Cichocki
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