Regularizaed extraction of non-negative latent factors from high-dimensional sparse matrices

Xin Luo, Shuai Li, Mengchu Zhou

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

With the exploration of the World Wide Web, more and more entities are involved in various online applications, e.g., recommender systems and social network services. In such context, high-dimensional sparse matrices describing the relationships among them are frequently encountered. It is highly important to develop efficient non-negative latent factor (NLF) models for these high-dimensional sparse relationships because of a) their ability to extract useful knowledge from them; b) their fulfillment of the non-negativity constraints for representing most non-negative industrial data; and c) their high computational and storage efficiency on high-dimensional sparse matrices. However, due to the imbalanced distribution of known data in such a matrix, it is necessary to investigate the regularization effect in NLF models. We first review the NLF model briefly. Then we propose to integrate the frequency-weight on each involved entity into its Tikhonov regularization terms, for representing the imbalanced data from a high-dimensional sparse matrix. Experimental results on industrial-size matrices indicate that the proposed scheme is effective in improving the performance of the NLF model in missing-data-estimation.

Original languageEnglish (US)
Title of host publication2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1221-1226
Number of pages6
ISBN (Electronic)9781509018970
DOIs
StatePublished - Feb 6 2017
Event2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Budapest, Hungary
Duration: Oct 9 2016Oct 12 2016

Publication series

Name2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings

Other

Other2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016
CountryHungary
CityBudapest
Period10/9/1610/12/16

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition
  • Artificial Intelligence
  • Control and Optimization
  • Human-Computer Interaction

Keywords

  • Frequency weight
  • High-dimentional sparse matrices
  • Latent factor
  • Non-negativity constraints
  • Regularization

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