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.
ASJC Scopus subject areas
- Physics and Astronomy(all)