Parametric Tensor Decompositions with Applications to DAP
The power of tensor decompositions in biomedical applications
... The epileptic seizure activity is known as an oscillatory phenomenon, con- sisting of rhythmical waves in a frequency band below 30Hz which evolve in amplitude, frequency and location [45]. Consider for example the ...
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Tensor Decompositions for Signal Processing Applications
... using tensor decom- positions are shown to have great flexibility in the choice of constraints that match data properties, and to find more general latent com- ponents in the data than matrix-based ...to ...
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Tensor decompositions for Face Recognition
... Introduction Natural images are composed by several factors such as illumination and view angle. Human perception remains robust despite these variations. To develop automatic proce- dures for Face Recognition is a ...
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The widespread use of multisensor technology and the emergence of big data. Tensor Decompositions. for Signal Processing Applications
... tensors. Decompositions of nonsymmetric tensors have been studied since the early 20th century [1], whereas the benefits of using more than two matrices in factor analysis (FA) [2] have been apparent in several ...
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Tensor Decompositions for Integral Histogram Compression and Look-Up
... Tensor Decompositions for Integral Histogram Compression and Look-Up Rafael Ballester-Ripoll, Student Member, IEEE, and Renato Pajarola, Member, IEEE Abstract—Histograms are a fundamental tool for ...
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Implementation of the full CCSDT electronic. structure model with tensor decompositions
... approximate tensor (2) t abc ijk we perform its “flattening”, ...full tensor t abc ijk to a desired size N SVD , one retains only those vectors U ai X that correspond to the largest singular values of the ...
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Evolution of communities of software: using tensor decompositions to compare software ecosystems
... JavaScript is another popular general-purpose interpreted programming language. It was first released in 1995 as part of the Netscape browser and was initially used for client-side scripts on web pages, but now has ...
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Learning fast dictionaries for sparse representations using low-rank tensor decompositions
... 17. Tsiligkaridis, T., Hero, A.O.: Covariance estimation in high dimensions via kro- necker product expansions. IEEE Transactions on Signal Processing 61(21), 5347– 5360 (2013) 18. Van Loan, C.F., Pitsianis, N.: ...
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When Are Overcomplete Topic Models Identifiable? Uniqueness of Tensor Tucker Decompositions with Structured Sparsity
... any parametric statistical model, identifiability is a fundamental question of whether the model parameters can be uniquely recovered given the observed ...of applications where the latent variables are the ...
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When Are Overcomplete Topic Models Identifiable? Uniqueness of Tensor Tucker Decompositions with Structured Sparsity
... any parametric statistical model, identifiability is a fundamental question of whether the model parameters can be uniquely recovered given the observed ...of applications where the latent variables are the ...
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Generic identifiability of tensor rank decompositions
... [COV2015] Chiantini, Ottaviani, and Vannieuwenhoven, On generic identifiability of symmetric tensors of subgeneric rank, Transactions of the American Mathematical Society, 2015. (Accepte[r] ...
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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 Decompositions with Banded Matrix Factors
... 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 decompositions for signal processing applications: from two-way to multiway component analysis
... Consequently, in signal processing and data analysis, CPD is typically used for factorizing data into easy to interpret components i.e., the rank-1 terms, while the goal of unconstrained[r] ...
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Tensor Decompositions for Learning Latent Variable Models
... of tensor decomposition methods to estimat- ing latent variable models, a significant departure from many previous approaches in the ma- chine learning and statistics ...
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Tensor Decompositions for Learning Latent Variable Models
... of tensor decomposition methods to estimat- ing latent variable models, a significant departure from many previous approaches in the machine learning and statistics ...
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Era of Big Data Processing: A New Approach via Tensor Networks and Tensor Decompositions
... the tensor and/or with an increasing accuracy of approximation for any choice of tensor network, that is, a tree (including TT and HT decompositions) ...(MERA) tensor networks can be ...core ...
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p 2 -Cycle decompositions of the tensor product of complete graphs
... cycle decompositions of K m × K n are completely settled by Balakrishnan et ...cycle decompositions in tensor products of complete multipartite graphs or of a complete graph and a complete bipartite ...
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Survey of hyperspectral image denoising methods based on tensor decompositions
... [19,20], tensor-algebra was brought to jointly analyze the 3D HSI, ...applying tensor algebra in reduc- ing noise in ...proposed tensor-based denosing methods utilize multilin- ear algebra to analyze ...
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Tensor decompositions and data fusion in epileptic EEG and fMRI data
... useful. Tensor decomposition techniques are very well suited to solve the BSS problem, as they provide a unique solution under mild ...framework, tensor-based analysis of real, large-scale clinical datasets ...
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