Smooth nonnegative matrix and tensor factorizations for robust multi-way data analysis[J] . Tatsuya Yokota,Rafal Zdunek,Andrzej Cichocki,Yukihiko Yamashita.Signal Processing . 2015T. Yokota, R. Zdunek, A. Cichocki, and Y. Yamashita, "Smooth non- negative matrix and tensor factorizations for ...
并不会覆盖每一部分内容,只会看和自己当前research有关的部分,主要是 nonnegative and low rank constraint下的 factorization。参考书:Cichocki A, Zdunek R, Phan A H, et al. Nonnegative matrix and tensor factorizations: applications to exploratory multi-way data analysis and blind source separation[M]...
Advances in Nonnegative Matrix and Tensor Factorization A. Cichocki,M. Mørup,P. Smaragdis,W. Wang,R. Zdunek Computational Intelligence and Neuroscience First Published:06 July 2008 Full text PDF Research Article Open Access Pattern Expression Nonnegative Matrix Factorization: Algorithm and Applications...
Link prediction on evolving data using matrix and tensor factorizations [C]//2009 IEEE International Conference on Data Mining Workshops. Miami: IEEE, 2009: 262–269. Chapter Google Scholar LEE D D, SEUNG H S. Algorithms for non-negative matrix factorization [M]//Advances in neural ...
Many methods have been proposed recently for high-dimensional data representation to reduce the dimensionality of the data. Matrix Factorization (MF) as an
Fast Local Algorithms for Large Scale Nonnegative Matrix and Tensor Factorizations alpha and beta divergencesNonnegative matrix factorization (NMF) and its extensions such as Nonnegative Tensor Factorization (NTF) have become prominent ... A Cichocki,AH Phan - 《Ieice Trans Fundamentals》 被引量: 35...
disadvantages:(i)3D tensor X has to be mapped through 3-mode flattening, also called unfolding and matricization, to matrix 3 1 2 (3) 0 I I I × + ∈ X ℝ whereas local structure of the image is lost; (ii) matrix factorization (3) = X AS employed by linear mixing models...
The key innovation of UINMF is the introduction of an unshared metagene matrixUto the iNMF objective function, incorporating features that belong to only one, or a subset, of the datasets when estimating metagenes and cell factor loadings. Previously, dataset integration using the iNMF algorithm...
The proposed method is experimentally evaluated on small image datasets, and the results demonstrate its superior performance than the state-of-the-art KNMF methods. 展开 关键词: Combined kernel Nonnegative matrix ...
In this paper, we present an on-line semi-supervised algorithm for real-time separation of speech and background noise. The proposed system is based on Nonnegative Matrix Factorization (NMF), where fixed speech bases are learned from training data wherea