Results 121 to 130 of about 3,137 (156)

Nonnegative Tucker Decomposition

2007 IEEE Conference on Computer Vision and Pattern Recognition, 2007
Nonnegative 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
openaire   +1 more source

Compression of hyperspectral images based on Tucker decomposition and CP decomposition

Journal of the Optical Society of America A, 2022
Hyperspectral 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, Jinsong, Zhou, Juanjuan, Jing, Lidong, Wei, Yacan, Li, Xiaoying, He, Lei, Feng, Boyang, Nie   +7 more
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Algorithms for Sparse Nonnegative Tucker Decompositions

Neural Computation, 2008
There 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.
Mørup, Morten, Hansen, Lars Kai, Arnfred, Sidse M.   +2 more
openaire   +3 more sources

D-Tucker: Fast and Memory-Efficient Tucker Decomposition for Dense Tensors

2020 IEEE 36th International Conference on Data Engineering (ICDE), 2020
Given 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.
Jun-Gi Jang, U Kang
openaire   +1 more source

Nonnegative Tucker decomposition with alpha-divergence

2008 IEEE International Conference on Acoustics, Speech and Signal Processing, 2008
Nonnegative 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, Andrzej Cichocki, Seungjin Choi   +2 more
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Wafer Pattern Recognition Using Tucker Decomposition

2019 IEEE 37th VLSI Test Symposium (VTS), 2019
In production test data analytics, it is often that an analysis involves the recognition of a conceptual pattern on a wafer map. A wafer pattern may hint a particular issue in the production by itself or guide the analysis into a certain direction. In this work, we introduce a novel approach to recognize patterns on a wafer map of pass/fail locations ...
Ahmed Wahba, Li-C. Wang, Zheng Zhang, Nik Sumikawa   +3 more
openaire   +1 more source

Parallel Tucker Decomposition with Numerically Accurate SVD

50th International Conference on Parallel Processing, 2021
Tucker 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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Double‐Tucker Decomposition and Its Computations

Numerical Linear Algebra with Applications
ABSTRACTThe famous Tucker decomposition has been widely and successfully used in many fields. However, it often suffers from the curse of dimensionality due to the core tensor and large ranks. To tackle this issue, we introduce an additional core tensor into Tucker decomposition and propose the so‐called double‐Tucker (dTucker) decomposition.
Mengyu Wang, Honghua Cui, Hanyu Li
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On optimizing distributed non-negative Tucker decomposition

Proceedings of the ACM International Conference on Supercomputing, 2019
The 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, Shivmaran S. Pandian, Saurabh Raje, Yogish Sabharwal   +3 more
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Supervised Nonnegative Tucker Decomposition for Computational Phenotyping

2019 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI), 2019
With the availability of Electronic Health Records (EHR) data, lots of predictive tasks in medical practice seem solvable by building predictive models. However, EHR data always contains various medical concepts (e.g., diagnosis, medicines, lab tests) with high dimensions and mass correlations among them.
Kai Yang, Wenqi Sun, Junfeng Zhao, Yasha Wang, Bing Xie   +4 more
openaire   +1 more source