SAGA: Sparse And Geometry-Aware non-negative matrix factorization through non-linear local embedding - Archive ouverte HAL Access content directly
Journal Articles Machine Learning Year : 2014

SAGA: Sparse And Geometry-Aware non-negative matrix factorization through non-linear local embedding

Abstract

This paper presents a new non-negative matrix factorization technique which (1) allows the decomposition of the original data on multiple latent factors accounting for the geometrical structure of the manifold embedding the data; (2) provides an optimal representation with a controllable level of sparsity; (3) has an overall linear complexity allowing handling in tractable time large and high dimensional datasets. It operates by coding the data with respect to local neighbors with non-linear weights. This locality is obtained as a consequence of the simultaneous sparsity and convexity constraints. Our method is demonstrated over several experiments, including a feature extraction and classification task, where it achieves better performances than the state-of-the-art factorization methods, with a shorter computational time.
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Dates and versions

hal-01018683 , version 1 (04-07-2014)

Identifiers

  • HAL Id : hal-01018683 , version 1

Cite

Nicolas Courty, Xing Gong, Jimmy Vandel, Thomas Burger. SAGA: Sparse And Geometry-Aware non-negative matrix factorization through non-linear local embedding. Machine Learning, 2014, pp.1--23. ⟨hal-01018683⟩
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