Concept

Rank factorization

Publications associées (7)

A Fast Gradient Method for Nonnegative Sparse Regression With Self-Dictionary

A nonnegative matrix factorization (NMF) can be computed efficiently under the separability assumption, which asserts that all the columns of the given input data matrix belong to the cone generated by a (small) subset of them. The provably most robust met ...

Ieee-Inst Electrical Electronics Engineers Inc2018

MATHICSE Technical Report : A fast gradient method for nonnegative sparse regression with self dictionary

Robert Gerhard Jérôme Luce

Nonnegative matrix factorization (NMF) can be computed efficiently under the separability assumption, which asserts that all the columns of the input data matrix belong to the convex cone generated by only a few of its columns. The provably most robust met ...

MATHICSE2016

Efficient Incremental Data Analysis

Milos Nikolic

Many data-intensive applications require real-time analytics over streaming data. In a growing number of domains -- sensor network monitoring, social web applications, clickstream analysis, high-frequency algorithmic trading, and fraud detections to name a ...

EPFL2016

LINVIEW: Incremental View Maintenance for Complex Analytical Queries

Christoph Koch, Milos Nikolic, Mohamed Elsayed Mohamed Ahmed El Seidy

Many analytics tasks and machine learning problems can be naturally expressed by iterative linear algebra programs. In this paper, we study the incremental view maintenance problem for such complex analytical queries. We develop a framework, called LINVIEW ...

2014

Computing symmetric nonnegative rank factorizations

An algorithm is described for the nonnegative rank factorization (NRF) of some completely positive (CP) matrices whose rank is equal to their CP-rank. The algorithm can compute the symmetric NRF of any nonnegative symmetric rank-r matrix that contains a di ...

Elsevier2012

Nonnegative Matrix Factorization and Spatial Covariance Model for Under-Determined Reverberant Audio Source Separation

Pierre Vandergheynst, Rémi Gribonval

We address the problem of blind audio source separation in the under-determined and convolutive case. The contribution of each source to the mixture channels in the time-frequency domain is modeled by a zero-mean Gaussian random vector with a full rank cov ...

2010

Recognition of generalized network matrices

Antoine Musitelli

In this thesis, we deal with binet matrices, an extension of network matrices. The main result of this thesis is the following. A rational matrix A of size n×m can be tested for being binet in time O(n6m). If A is binet, our algorithm outputs a nonsingular ...

EPFL2007

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