[PDF] Top 20 On minimal decompositions of low rank symmetric tensors
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On minimal decompositions of low rank symmetric tensors
... the rank of f ? Can we provide a minimal Waring decomposition? For general forms of fixed degree and fixed number of variables, the value of the rank is known due to the ... Voir le document complet
27
Decomposition of homogeneous polynomials with low rank
... class of a form (a symmetric tensor) of type ...non-maximal symmetric border rank s using the notion of (s − 1)- weakly non-defectivity introduced by ...particular ... Voir le document complet
9
Mutual information for symmetric rank-one matrix estimation: A proof of the replica formula
... Factorizing low-rank matrices has many applications in machine learning and ...the symmetric rank-one case. This allows to express the minimal mean-square-error and to characterize the ... Voir le document complet
14
Canonical polyadic decomposition of 3rd order semi-nonnegative semi-symmetric tensors using LU and QR matrix factorizations
... sum of rank- one terms ...a minimal number of rank-one terms which are needed to exactly fit a given HO ...multitude of methods were developed to compute the CP decompo- ... Voir le document complet
34
Real and complex rank for real symmetric tensors with low complex symmetric rank
... TENSORS WITH LOW COMPLEX SYMMETRIC RANK EDOARDO BALLICO, ALESSANDRA BERNARDI ...case of real homogeneous polynomial P whose min- imal real and complex decompositions in terms ... Voir le document complet
9
Compressive PCA for Low-Rank Matrices on Graphs
... 1) They do not recover non-linear low-rank structures. 2) They do not scale for big datasets Y 2 < p⇥n (large p and large n, where p is the number of features). Many high dimensional datasets lie ... Voir le document complet
18
Sparse Representations and Low-Rank Tensor Approximation
... usefulness of the proposed approach is demonstrated in Section IV, where several applications are pointed out, with an emphasis on the problem of joint localization and estimation of radiating ... Voir le document complet
19
Constant Modulus Algorithms via Low-Rank Approximation
... works of Godard [1] and Triechler and Agee [2] on blind ...modulus of the desired signal, without any explicit learning of the chan- nel impulse response, and was therefore referred as ...presence ... Voir le document complet
12
Parametric PDEs: Sparse or low-rank approximations?
... class of parametric operator equations where the involved parame- ters could either be of deterministic or stochastic ...use low-rank approximations of solutions based on a separation ... Voir le document complet
43
Sparse and low-rank multivariate Hawkes processes
... events of users (referred to as nodes in the following) that are fully identified through their corresponding user id and ...structure of data consisting in a sequence of independent cascades, ... Voir le document complet
33
Probabilistic low-rank matrix completion on finite alphabets
... value of the unknown matrix. Finally, the rates of convergence given by Theorem 2 are faster than the rates of convergence obtained in [8] and ...norm of the matrix probabilities ... Voir le document complet
18
Deterministic algorithms for the low rank approximation of matrices
... Low rank approximations using the symmetric eigenvalue decomposition Objectives and key idea Formulation of the matrix eigenvalue problem Standard algorithm for the Hermitian eigendecomp[r] ... Voir le document complet
115
Stratification of the fourth secant variety of Veronese variety via the symmetric rank
... sets of points computing r X 3,3 (P ) for points P ∈ σ 4 (X 3,3 ) of maximal X 3,3 ...details of the proof we need some preliminary and auxiliary ...schemes of P m to two 0-dimensional ... Voir le document complet
22
Low-rank and sparse recovery of human gait data
... ]) of missing ...positions of a minimum of three non-collinear markers on each body segment to reconstruct the transformation ...recovery of missing marker ...properties of human ... Voir le document complet
13
Symmetric tensor rank with a tangent vector: a generic uniqueness theorem
... field of dimension m + 1 respectively), 1 ≤ i ≤ t, that are uniquely determined (up to a ...question of the uniqueness of a particular decomposition for certain given homogeneous ...terms of ... Voir le document complet
9
Line search and trust region strategies for canonical decomposition of semi-nonnegative semi-symmetric 3rd order tensors
... combination of tissue spectra, which are as independent as possible ...mixture of sound waves computed by means of the constant-Q transform (Fourier transform with log-frequency) of a temporal ... Voir le document complet
38
Low Rank Matrix Completion with Exponential Family Noise
... Keywords: Low rank matrix estimation; matrix completion; exponential family model; nuclear norm ...servations of its entries. This problem arises in a wide range of practical situations such ... Voir le document complet
21
A Riemannian Newton Optimization Framework for the Symmetric Tensor Rank Approximation Problem
... THE SYMMETRIC TENSOR RANK APPROXIMATION PROBLEM ∗ RIMA KHOUJA † ‡ , HOUSSAM KHALIL † , AND BERNARD MOURRAIN ‡ ...The symmetric tensor rank approximation problem (STA) consists in computing the ... Voir le document complet
25
Kolmogorov widths and low-rank approximations of parametric elliptic PDEs
... behavior of the Legendre coefficients and singular values for the original solution map y 7→ u(y), it is extremely important to resolve the subdomain interfaces appropriately in the spatial ...the symmetric ... Voir le document complet
28
Fast and Memory Optimal Low-Rank Matrix Approximation
... version of M (k) , this condition quantifies the maximum amount of noise allowed for our algorithm to be asymptotically ...any rank k approximation algorithm needs to at least store its output, ... Voir le document complet
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