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Found 8 papers, 0 papers with code
Algebraic-Combinatorial Methods for Low-Rank Matrix Completion with Application to Athletic Performance Prediction
This paper presents novel algorithms which exploit the intrinsic algebraic and combinatorial structure of the matrix completion task for estimating missing en- tries in the general low rank setting.
Learning with Cross-Kernels and Ideal PCA
We describe how cross-kernel matrices, that is, kernel matrices between the data and a custom chosen set of `feature spanning points' can be used for learning.
Matroid Regression
At the heart of our approach is the so-called regression matroid, a combinatorial object associated to sparsity patterns, which allows to replace inversion of the large matrix with the inversion of a kernel matrix that is constant size.
Dual-to-kernel learning with ideals
In this paper, we propose a theory which unifies kernel learning and symbolic algebraic methods.
Error-Minimizing Estimates and Universal Entry-Wise Error Bounds for Low-Rank Matrix Completion
We propose a general framework for reconstructing and denoising single entries of incomplete and noisy entries.
Obtaining error-minimizing estimates and universal entry-wise error bounds for low-rank matrix completion
We propose a general framework for reconstructing and denoising single entries of incomplete and noisy entries.
Coherence and sufficient sampling densities for reconstruction in compressed sensing
We give a new, very general, formulation of the compressed sensing problem in terms of coordinate projections of an analytic variety, and derive sufficient sampling rates for signal reconstruction.
The Algebraic Combinatorial Approach for Low-Rank Matrix Completion
We present a novel algebraic combinatorial view on low-rank matrix completion based on studying relations between a few entries with tools from algebraic geometry and matroid theory.
