نتایج جستجو برای: nonnegative tensor
تعداد نتایج: 52198 فیلتر نتایج به سال:
Tensors can be viewed as multilinear arrays or generalizations of the notion of matrices. Tensor decompositions have applications in various fields such as psychometrics, signal processing, numerical linear algebra and data mining. When the data are nonnegative, the nonnegative tensor factorization (NTF) better reflects the underlying structure. With NTF it is possible to extract information fr...
Matrix factorizations and their extensions to tensor factorizations and decompositions have become prominent techniques for linear and multilinear blind source separation (BSS), especially multiway Independent Component Analysis (ICA), Nonnegative Matrix and Tensor Factorization (NMF/NTF), Smooth Component Analysis (SmoCA) and Sparse Component Analysis (SCA). Moreover, tensor decompositions hav...
A tensor singular value decomposition based upon the *-product defined on third-order tensors by Kilmer, Martin and Perrone provides an effective means of generalizing Principal Component Analysis. This talk will focus on tensor eigendecompostions and generalized eigendecompostions under the *-product. Discusion will include an overview of the theoretical underpinings, some computational concer...
In this paper we propose a new flexible group tensor analysis model called the linked CP tensor decomposition (LCPTD). The LCPTD method can decompose given multiple tensors into common factor matrices, individual factor matrices, and core tensors, simultaneously. We applied the Hierarchical Alternating Least Squares (HALS) algorithm to the LCPTD model; besides we impose additional constraints t...
In this paper we develop a new model and propose an iterative method to calculate stationary probability vector of a transition probability tensor arising from a higher-order Markov chain. Existence and uniqueness of such stationary probability vector are studied. We also discuss and compare the results of the new model with those by the eigenvector method for a nonnegative tensor. Numerical ex...
In this paper we propose a quadratically convergent algorithm for finding the largest eigenvalue of a nonnegative homogeneous polynomialmapwhere theNewtonmethod is used to solve an equivalent system of nonlinear equations. The semi-symmetric tensor is introduced to reveal the relation between homogeneous polynomial map and its associated semi-symmetric tensor. Based on this relation a globally ...
Tensor decomposition is an effective tool for learning multi-way structures and heterogeneous features from high-dimensional data, such as the multi-view images multichannel electroencephalography (EEG) signals, are often represented by tensors. However, most of tensor methods linear feature extraction techniques, which unable to reveal nonlinear structure within data. To address problem, a lot...
Nonnegative factorization of tensors plays an important role in the analysis of multi-dimensional data in which each element is inherently nonnegative. It provides a meaningful lower rank approximation, which can further be used for dimensionality reduction, data compression, text mining, or visualization. In this paper, we propose a fast algorithm for nonnegative tensor factorization (NTF) bas...
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