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Online Kernel Matrix Factorization

  • Andrés Esteban Páez-TorresEmail author
  • Fabio A. González
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9423)

Abstract

Matrix factorization (MF) has shown to be a competitive machine learning strategy for many problems such as dimensionality reduction, latent topic modeling, clustering, dictionary learning and manifold learning, among others. In general, MF is a linear modeling method, so different strategies, most of them based on kernel methods, have been proposed to extend it to non-linear modeling. However, as with many other kernel methods, memory requirements and computing time limit the application of kernel-based MF methods in large-scale problems. In this paper, we present a new kernel MF (KMF). This method uses a budget, a set of representative points of size \(p\ll n\), where n is the size of the training data set, to tackle the memory problem, and uses stochastic gradient descent to tackle the computation time and memory problems. The experimental results show a performance, in particular tasks, comparable to other kernel matrix factorization and clustering methods, and a competitive computing time in large-scale problems.

Keywords

Feature space factorization Kernel matrix factorization Large-scale learning 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Andrés Esteban Páez-Torres
    • 1
    Email author
  • Fabio A. González
    • 1
  1. 1.MindLab Research GroupUniversidad Nacional de ColombiaBogotáColombia

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