Results 71 to 80 of about 130 (119)
IEEE Journal of Selected Topics in Applied Earth Observations and Remote SensingThe purpose of hyperspectral unmixing (HU) is to extract the spectral signatures and their proportion fractions from the hyperspectral remote sensing image (HSIs), which is a crucial issue in HSIs processing.
Kewen Qu +4 more
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Spatiotemporal water quality data reconstruction: A tensor factorization framework
Ecological InformaticsAutomatic high-frequency monitoring (AHFM) of water quality parameters has gained growing attention for managing eutrophic lakes. However, missing data in water quality datasets remains a persistent challenge, often compromising the reliability of ...
Xuke Wu +4 more
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Advances in Nonnegative Matrix and Tensor Factorization
Computational Intelligence and Neuroscience, 2008 Andrzej Cichocki +4 more
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Nonnegative matrix and tensor factorization
Nonnegative matrix and tensor factorizationSeveral approaches to solve a nonnegative matrix factorization/ nonnegative tensor factorization (NMF/NTF) problems have been outlined. These methods include multiplicative algorithms, projected gradient (PG), fixed point alternating least squares (FP-ALS) algorithms, quasi-Newton (QN) algorithm, and multilayer technique.
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Nonnegative Matrix and Tensor Factorization for Discussion Tracking
, 2009 Brett Bader +2 more
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Fast Nonnegative Tensor Factorizations With Tensor Train Model
Lobachevskii Journal of Mathematics, 2022zbMATH Open Web Interface contents unavailable due to conflicting licenses.
E E Tyrtyshnikov
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NONNEGATIVE TENSOR FACTORIZATION FOR CONTINUOUS EEG CLASSIFICATION
International Journal of Neural Systems, 2007In 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 ...
Hyekyoung Lee +3 more
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Variable Selection for Efficient Nonnegative Tensor Factorization
2015 IEEE International Conference on Data Mining, 2015Nonnegative Tensor Factorization (NTF) has become a popular tool for extracting informative patterns from tensor data. However, NTF has high computational cost both in space and in time, mostly in iterative calculation of the gradient. In this paper, we consider variable selection to reduce the cost, assuming sparsity of the factor matrices.
Keigo Kimura, Mineichi Kudo
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Nonnegative Tensor Factorization Accelerated Using GPGPU
IEEE Transactions on Parallel and Distributed Systems, 2011This 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 ...
Jukka Antikainen +5 more
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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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