Low rank matrix estimation
Constrained Low-rank Matrix Estimation: Phase Transitions, Approximate Message Passing and Applications
... for Low-Rank matrix estimation ( Low-RAMP): In section II we derive and detail the approximate message passing algorithm to estimate marginal probabilities of the probability measures ...
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Adaptive filtering for estimation of a low-rank positive semidefinite matrix
... for estimation of a low-rank positive semidefinite matrix ...for low-rank matrices find applications in high-dimensional distance learning problems for classification or ...
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Low Rank Matrix Completion with Exponential Family Noise
... Keywords: Low rank matrix estimation; matrix completion; exponential family model; nuclear norm ...the matrix completion problem one aims at recovering a matrix, based on ...
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Mutual information for symmetric rank-one matrix estimation: A proof of the replica formula
... beyond low-rank matrix ...on rank-one symmetric matrix estimation, our proof technique is readily extendable to more generic low-rank symmetric matrix or ...
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Rank-one matrix estimation: analysis of algorithmic and information theoretic limits by the spatial coupling method
... beyond low-rank matrix estimation and can be applied for a wide range of inference problems where message-passing algorithm and spatial coupling can be ...
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Covariance estimation on matrix manifolds
... new low-rank parameterized covariance families, based on given problem-specific anchor covariance ...The rank of the anchor matrices is assumed to be equal to some value r, usually much smaller than ...
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Budget-Optimal Crowdsourcing Using Low-Rank Matrix Approximations
... As a taskmaster, the natural core optimization problem of our concern is how to achieve a certain reliability in our answers with minimum cost. Since we pay equal amount for all the task assignments, the cost is ...
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On a fixed-point algorithm for structured low-rank approximation and estimation of half-life parameters
... Terms— Low rank approximation, structured ma- trices, fixed-point ...given matrix F by a structured matrix ...is low rank and Positive Semi-Definite (PSD). While structured ...
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Estimation of structured tensor models and recovery of low-rank tensors
... traditional matrix-based ones. The estimation of excitation/emission spectra from fluorescence data in chemometrics by means of high-order tensor decomposition techniques [ 25 , 24 ] is a stereotypical ...
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Phase-constrained Parallel Magnetic Resonance Imaging Reconstruction Based on Low-rank Matrix Completion
... There are many benefits when combining the phase con- straint with parallel imaging in a comprehensive method for MRI. Less data acquisition and better reconstruction result are the most two significant advantages. In ...
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PAC-Bayesian estimation of low-rank matrices
... 70 CHAPTER 3. QUANTUM STATE TOMOGRAPHY independent systems identically prepared in the same state. The ’tomographic’ method, also named as linear/direct inversion [ Vogel and Risken , 1989 ; Reh´ ˇ aˇ cek et al. , 2010 ...
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Probabilistic low-rank matrix completion on finite alphabets
... Table 4: Binomial prediction error when performing one versus the others procedure on the Movie- Lens 100k dataset. 4 Conclusion and future work We have proposed a new nuclear norm penalized maximum log-likelihood ...
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Constant Modulus Algorithms via Low-Rank Approximation
... as rank-1 matrix minimization problems, and then transforming them to convex optimization problems by replacing the rank-1 constraint by its convex surrogate - the minimization of the trace ...
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Decomposition of Low Rank Multi-Symmetric Tensor
... the matrix H τ associated to T in the bases A 1 and A 2 and we substitute x 0 by ...Hankel matrix associated to a chosen truncated bases of A 1 and A 2 such that the multiplication of the matrix by ...
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Sparse Representations and Low-Rank Tensor Approximation
... λ r E (r) ) (3) In particular, decomposable tensors have a rank equal to one. When minimal, decomposition (2) reveals the rank and is often called the Canonical Polyadic (CP) decomposition of T. Other ...
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Decomposition of homogeneous polynomials with low rank
... 0 , . . . , x m ] 1 ) ' P(V ∗ ), the generic element belonging to σ s (X m,d ) is the projective class of a form (a symmetric tensor) of type (1). Unfortunately, for a given P ∈ P N , we only have the inequality sbr(P ) ...
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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 result of ...
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Estimation of Covariance Matrix Distances in the High Dimension Low Sample Size Regime
... The objective is to depict the data in a two-dimensional pro- jection space in order to identify the two classes based on their covariance matrices. To this end, we use kernel PCA [5] for a Wasserstein distance metric, ...
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A Proposition for Fixing the Dimensionality of a Laplacian Low-rank Approximation of any Binary Data-matrix
... reference matrix seem to have been discovered independently several times, in various application domains: ecology, psychometrics, combinatorics, ...Boolean matrix can be con- verted into any other one with ...
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Random matrix-improved estimation of covariance matrix distances
... [15] Houdard, A., Bouveyron, C., Delon, J., 2017. High-dimensional mixture models for unsupervised image denoising (hdmi) . [16] Kammoun, A., Couillet, R., Najim, J., Debbah, M., 2012. Performance of mutual information ...
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