Verification of effectiveness of a probabilistic algorithm for latent structure extraction using an associative memory model

Kensuke Wakasugi, Tatsu Kuwatani, Kenji Nagata, Hideki Asoh, Masato Okada

Research output: Contribution to journalArticlepeer-review

Abstract

Multivariate analysis techniques are widely used to analyze high-dimensional data in many fields of scientific research. In most cases, however, analysis is conducted under the assumption that there is a priori knowledge of the form of the latent structures in the data. Recently, Kemp and Tenenbaum proposed a new method of analysis that can select the forms from a set of several primitive forms and structures from a set of latent structures for those forms. It is important to evaluate the validity and the effectiveness of the proposed method by using synthetic data sets so that we can control their form. In this study, we apply the Kemp-Tenenbaum method to synthetic data sets that are artificially generated by an associative memory model. The forms and the structures that had been embedded in the data sets were successfully reconstructed, which demonstrates the validity of the Kemp-Tenenbaum method.

Original languageEnglish
Article number104801
Journaljournal of the physical society of japan
Volume83
Issue number10
DOIs
Publication statusPublished - 2014 Oct 15

ASJC Scopus subject areas

  • Physics and Astronomy(all)

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