tensor models
Estimation of structured tensor models and recovery of low-rank tensors
... applied tensor-based techniques with data analysis purposes [ 200 , 201 , 202 , 100 , 39 ...the tensor structure of higher-order cumulants [ 36 , 38 , 37 , 50 ], while many works employing tensor ...
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Community detection in hypergraphs, spiked tensor models, and Sum-of-Squares
... goemans@math.mit.edu May 9, 2017 Abstract We study the problem of community detection in hypergraphs under a stochastic block model. Similarly to how the stochastic block model in graphs suggests studying spiked random ...
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Passed & Spurious: analysing descent algorithms and local minima in Spiked Matrix-Tensor Models
... 6 Discussion We analyzed the behavior of two descent algorithms in optimizing a rough high-dimensional loss landscape of the spiked matrix-tensor model. We used the Kac-Rice formula to count the average number of ...
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Brain source imaging: from sparse to tensor models
... B. Tensor-based source localization – assumption SpTe), assumption Sp5), Te3), or Te4), assumptions Sp4) and Sp3) The objective of tensor-based methods consists in identifying the lead-field vectors and the ...
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Tensor-based MIMO relaying communication systems
... Recently, tensor decompositions have been introduced as an efficient approach for channel estimation in cooperative com- munication ...PARAFAC tensor decomposition for modeling the received signals did not ...
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Complex Energy Landscapes in Spiked-Tensor and Simple Glassy Models: Ruggedness, Arrangements of Local Minima, and Phase Transitions
... 1. Option A This is the case found, e.g., for spiked-tensor models such as p ¼ k ¼ 3 and p ¼ k ¼ 4. For concreteness, we focus on p ¼ k ¼ 3 [p ¼ k ¼ 4 is analogous, but one has to take into account that f k ...
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Symmetric tensors and symmetric tensor rank
... tensor of order k and dimension n may be uniquely associated with a homogeneous polynomial of degree k in n variables. Recall that these are just polynomials in n vari- ables whose constituting monomials all have ...
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Wideband multiple diversity tensor array processing
... the tensor wideband approach for source estimation and localization stems from the need to focus every frequency contribution on the same ...apply tensor decomposition techniques to wideband ...narrowband ...
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Tensor Decompositions with Banded Matrix Factors
... Abstract The computation of the model parameters of a Canonical Polyadic Decom- position (CPD), also known as the parallel factor (PARAFAC) or canonical decomposition (CANDECOMP) or CP decomposition, is typically done by ...
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Tensor-based regression models and applications
... coefficient tensor but still resorts to an alternating strategy to estimate projecting factors during the projection ...of tensor regression studies are dedicated to the batch setting where the entire ...
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A Riemannian Framework for Tensor Computing
... sampled tensors (e.g. for linear to tri-linear interpolation), the problem proved to be more difficult for irregularly sampled values. The solution we propose is to consider this type of interpolation as a statistical ...
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Effective diffusion tensor computed by homogenization
... L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou ...
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Effective Diffusion Tensor Computed by Homogenization
... S/V=0,34 μm -1 . The average radius of cells is 3.9 μm for 2D and 8,8μm for 3D. We simulated the DMRI signal for D=2,8.10 -3 μm 2 / μs and κ=10 -5 μm/μs and κ=10 -4 μm/μs, giving computed ADCs of between 0,5.10 -3 and ...
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Approximate tensor diagonalization by invertible transforms
... the tensor, such as to min- imize the sum of squares of the off-diagonal ...the Tensor Approximate Fitting (TAF) prob- lem, each nonsingular matrix acts on every mode of the tensor, so as to minimize ...
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Probabilistic tensor factorization and model selection
... 2 Probabilistic Tensor Factorization In order to simplify notation, and without loss of generality, we focus on a third-order tensor X ∈ R I×J ×K . A factorization of X is conveniently expressed using the ...
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Gauged supergravities, tensor hierarchies, and M-theory
... antisymmetric tensor fields with an initially space-time dependent embedding tensor Θ(x), in such a way that the (D−1)- and D-forms, respectively, impose the constancy of Θ, and the closure of the ...
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2016 — Abnormal behavior detection using tensor factorization
... The tensor represents the network topology which is abstracted from a directed graph, consisting of nodes and ...nodes.The tensor structure allows incorpo- ration of temporal ...the tensor consists ...
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Descent and Forms of Tensor Categories
... a tensor product of quaternion algebras over K. Statement (v) follows from a well known result of global class field theory (see [AT, p.105], [E, Theorem 3.6]) saying that any element of order 2 in Br(K) for ...
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Assessment of spinal somatosensory systems with diffusion tensor imaging in syringomyelia: spinal diffusion tensor imaging in syringomyelia
... INTRODUCTION Diffusion tensor imaging (DTI) is a technique that uses MRI to evaluate the movement of extracellular water molecules within the white matter fibers. 1 2 Using specialized fiber-tracking (FT) ...
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Mathematical Morphology on Tensor Images for Fiber Enhancement
... MATHEMATICAL MORPHOLOGY ON TENSOR IMAGES FOR FIBER ENHANCEMENT S. Blusseau 1,2 , I. Bloch 2 , Y. Gousseau 2 , S. Velasco-Forero 1 , J. Angulo 1 1: CMM, Mines ParisTech, PSL Research University, France 2: LTCI, T ...
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