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Distributed and Randomized Tensor Train Decomposition for Feature Extraction.

, and . IJCNN, page 1-8. IEEE, (2019)

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Data Clustering with Semi-binary Nonnegative Matrix Factorization.. ICAISC, volume 5097 of Lecture Notes in Computer Science, page 705-716. Springer, (2008)Initialization of Nonnegative Matrix Factorization with Vertices of Convex Polytope.. ICAISC (1), volume 7267 of Lecture Notes in Computer Science, page 448-455. Springer, (2012)Blind Image Separation Using Nonnegative Matrix Factorization with Gibbs Smoothing., and . ICONIP (2), volume 4985 of Lecture Notes in Computer Science, page 519-528. Springer, (2007)Extended SMART Algorithms for Non-negative Matrix Factorization ., , , , , and . ICAISC, volume 4029 of Lecture Notes in Computer Science, page 548-562. Springer, (2006)Image Completion with Filtered Low-Rank Tensor Train Approximations., , and . IWANN (2), volume 11507 of Lecture Notes in Computer Science, page 235-245. Springer, (2019)Fast Recursive Nonnegative Standard and Hierarchical Tucker Decomposition., and . IEEE Signal Process. Lett., 26 (9): 1265-1269 (2019)Randomized Nonnegative Tensor Factorization for Feature Extraction from High-dimensional Signals., and . IWSSIP, page 1-5. IEEE, (2018)Smooth nonnegative matrix and tensor factorizations for robust multi-way data analysis., , , and . Signal Process., (2015)Improved M-FOCUSS Algorithm With Overlapping Blocks for Locally Smooth Sparse Signals., and . IEEE Trans. Signal Process., 56 (10-1): 4752-4761 (2008)Kaczmarz extended algorithm for tomographic image reconstruction from limited-data., and . Math. Comput. Simul., 65 (6): 579-598 (2004)