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Found 7 papers, 1 papers with code
Performance Gaps in Multi-view Clustering under the Nested Matrix-Tensor Model
We study the estimation of a planted signal hidden in a recently introduced nested matrix-tensor model, which is an extension of the classical spiked rank-one tensor model, motivated by multi-view clustering.
On the Accuracy of Hotelling-Type Asymmetric Tensor Deflation: A Random Tensor Analysis
This work introduces an asymptotic study of Hotelling-type tensor deflation in the presence of noise, in the regime of large tensor dimensions.
Hotelling Deflation on Large Symmetric Spiked Tensors
This paper studies the deflation algorithm when applied to estimate a low-rank symmetric spike contained in a large tensor corrupted by additive Gaussian noise.
Majorization-minimization for Sparse Nonnegative Matrix Factorization with the $β$-divergence
It is well known that the norm of the other factor (the dictionary matrix) needs to be controlled in order to avoid an ill-posed formulation.
A Random Matrix Perspective on Random Tensors
A numerical verification provides evidence that the same holds for orders 4 and 5, leading us to conjecture that, for any order, our fixed-point equation is equivalent to the known characterization of the ML estimation performance that had been obtained by relying on spin glasses.
On the Realization of Impulse Invariant Bilinear Volterra Kernels
As previously shown, the direct extension of the impulse invariance principle to Volterra kernels has to be modified in order to provide a condition for the exact modeling of mixed-signal chains.
Joint Majorization-Minimization for Nonnegative Matrix Factorization with the $β$-divergence
Our new updates are derived from a joint majorization-minimization (MM) scheme, in which an auxiliary function (a tight upper bound of the objective function) is built for the two factors jointly and minimized at each iteration.
