Results 31 to 40 of about 1,665,186 (233)
Community Detection on Mixture Multi-layer Networks via Regularized Tensor Decomposition [PDF]
Annals of Statistics, 2020 We study the problem of community detection in multi-layer networks, where pairs of nodes can be related in multiple modalities. We introduce a general framework, i.e., mixture multi-layer stochastic block model (MMSBM), which includes many earlier ...
Bing-Yi Jing +3 more
semanticscholar +1 more source
Randomized CP tensor decomposition
Machine Learning: Science and Technology, 2020 Abstract The CANDECOMP/PARAFAC (CP) tensor decomposition is a popular dimensionality-reduction method for multiway data. Dimensionality reduction is often sought after since many high-dimensional tensors have low intrinsic rank relative to the dimension of the ambient measurement space.
N Benjamin Erichson +3 more
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Tensor-CUR Decompositions for Tensor-Based Data [PDF]
SIAM Journal on Matrix Analysis and Applications, 2006 Motivated by numerous applications in which the data may be modeled by a variable subscripted by three or more indices, we develop a tensor-based extension of the matrix CUR decomposition. The tensor-CUR decomposition is most relevant as a data analysis tool when the data consist of one mode that is qualitatively different from the others. In this case,
Michael W. Mahoney +2 more
openaire +1 more source
DAO-CP: Data-Adaptive Online CP decomposition for tensor stream
PLoS ONE, 2022 How can we accurately and efficiently decompose a tensor stream? Tensor decomposition is a crucial task in a wide range of applications and plays a significant role in latent feature extraction and estimation of unobserved entries of data. The problem of
Sangjun Son +3 more
doaj +2 more sources
Symmetric tensor decomposition
Linear Algebra and its Applications, 2010 Publication in the conference proceedings of EUSIPCO, Glasgow, Scotland ...
Brachat, Jérôme +3 more
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A Survey on Tensor Techniques and Applications in Machine Learning
IEEE Access, 2019 This survey gives a comprehensive overview of tensor techniques and applications in machine learning. Tensor represents higher order statistics. Nowadays, many applications based on machine learning algorithms require a large amount of structured high ...
Yuwang Ji, Qiang Wang, Xuan Li, Jie Liu
doaj +1 more source
Legendre decomposition for tensors*
Journal of Statistical Mechanics: Theory and Experiment, 2019 Abstract We present a novel nonnegative tensor decomposition method, called Legendre decomposition, which factorizes an input tensor into a multiplicative combination of parameters. Thanks to the well-developed theory of information geometry, the reconstructed tensor is unique and always minimizes the KL divergence from an input tensor ...
Sugiyama, Mahito +2 more
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A constructive arbitrary-degree Kronecker product decomposition of tensors [PDF]
, 2016 We propose the tensor Kronecker product singular value decomposition~(TKPSVD) that decomposes a real $k$-way tensor $\mathcal{A}$ into a linear combination of tensor Kronecker products with an arbitrary number of $d$ factors $\mathcal{A} = \sum_{j=1}^R ...
Batselier, Kim, Wong, Ngai
core +2 more sources
IEEE Access, 2018 Reliable detection and recovery of a microseismic event in large volume of passive monitoring data is usually a challenging task due to the low signal-to-noise ratio environment.
Naveed Iqbal +5 more
doaj +1 more source
Cohomology and Decomposition of Tensor Product Representations of SL(2,R) [PDF]
, 2002 We analyze the decomposition of tensor products between infinite dimensional (unitary) and finite-dimensional (non-unitary) representations of SL(2,R). Using classical results on indefinite inner product spaces, we derive explicit decomposition formulae,
André van Tonder +26 more
core +4 more sources