The Tensor Product Decomposition in G
The tilting tensor product theorem and decomposition numbers for symmetric groups
... The representation theory of the symmetric group Σ d over k is also poorly understood. For each partition λ there is a Specht module S λ , and determining decomposition numbers of these would be sufficient to ...
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Structured Tensor Recovery and Decomposition
... ID, product ID and contextual variables including time, location and so on; in computer vision and graphics [LMWY09], visual data are naturally indexed by the specifications in space, frequency channel, time ...
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Decomposition of Finite Schmidt Rank Bounded Operators on the Tensor Product of Separable Hilbert Spaces
... the tensor product to develop a new algo- rithm to compute Schmidt decompositions of FSR bounded operators on the tensor product of separable Hilbert spaces with finite or infinite ...Schmidt ...
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Singleshot : a scalable Tucker tensor decomposition
... Remark 1. . For one-mode subtensors, it is relevant to choose n such that I n is the largest dimension since this yields the smallest subtensors. We stress that all entries of the tensor X have been entirely ...
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Distributed Large-Scale Tensor Decomposition
... The need for large-scale tensor computations is increasing and there is a huge gap to be filled. In contrast to large-scale matrix factorization, very few works are devoted to large-scale tensors. Two ways have ...
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Separable Filter Learning with Tensor Decomposition
... exist decomposition methods [19] such as Canonical Polyadic De- composition ...R-rank tensor can be factorized into a sum of R rank-one tensors (a tensor that can be represented as the outer ...
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Tensor rank is not multiplicative under the tensor product
... rank decomposition of a tensor t can be transformed into a tensor rank decomposition of tensor powers of t with a penalty that depends on the so-called error degree of the ...
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DECOMPOSITION OF TENSOR PRODUCTS OF IRREDUCIBLE UNITARY REPRESENTATIONS
... the tensor product of an irreducible uni- tary representation of a (discrete) group G and an zz-dimensional (n < °°) unitary representation of G decomposes into at most n irreducible ...
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Decomposition of Low Rank Multi-Symmetric Tensor
... Symmetric Tensor Decomposition Problem In this section we give the definition of a multi-symmetric tensor as a multi-homogeneous polynomial of a different positive degree at each collection of ...
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Tensor product dual frames
... The tensor product duals of tensor product frames can provide a rank-one decomposition of bounded antilinear operators between two Hilbert ...addresses tensor product dual ...
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Tensor Ring Decomposition with Rank Minimization on Latent Space: An Efficient Approach for Tensor Completion
... matrix product state/tensor-train (MPS/TT) decomposition has be- come very attractive, owing to its super-compression and computational efficiency properties (Oseledets ...TT decomposition, ...
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Decomposition of spaces of distributions induced by tensor product bases
... An important reason for introducing admissible cutoff functions of second kind is that such cutoff functions with “small” derivatives Section 3.4, Section 3.6 allow to construct tensor p[r] ...
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Clustering patients with tensor decomposition
... a tensor-decomposition algorithm to retrieve asymptotically convergent estimates of the unknown parameters, uses this decomposition procedure to generate approximate parameters that are then used to ...
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2 Background on Tensor Decomposition
... In addition, most of the existing methods lack a principled way to discover latent clusters, which is important for better understanding of the data. To address these issues, we propose a scalable nonparametric ...
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Skew-Symmetric Tensor Decomposition
... σ4 G(3, C 7 ) hence such a tensor has skew-symmetric rank ...4 tensor, it is sufficient to take an element of the form (9) and add to it any random rank 1 element of V3 C 7 ...a decomposition ...
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Applications of Wavelet Transform and Tensor Decomposition
... TUCKER DECOMPOSITION Compression of hyperspectral images is performed in four ...four tensor. For each tensor the size of unknown core tensor (J1 × J2 × J3) is selected ...approximate ...
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Tensor Decomposition in Multiple Kernel Learning
... algorithms; this study focuses on two major MKL approaches based on the structured risk objectives [8] and kernel alignment algorithms [9]. Then Section 3 introduces the kernel learning framework, MKL algorithms and its ...
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Tensor Decomposition for Multilayer Networks Clustering
... Single source data clustering The clustering method can be mainly classified into three scenarios: feature-based, graph-based, and hybrid clustering method. The well-known feature-based clustering methods include the ...
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Perturbation theory for the tensor rank decomposition
... [COV14] Chiantini, Ottaviani, and Vannieuwenhoven, An algorithm for generic and low-rank specific identifiability of complex tensors , SIAM J. Appl., 2014[r] ...
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