Comparison of Seven Methods for Boolean Factor Analysis and Their Evaluation by Information Gain

0382475 - ÚI 2016 RIV US eng J - Článek v odborném periodiku
Frolov, A. - Húsek, Dušan - Polyakov, P.Y.
Comparison of Seven Methods for Boolean Factor Analysis and Their Evaluation by Information Gain.
IEEE Transactions on Neural Networks and Learning Systems. Roč. 27, č. 3 (2016), s. 538-550. ISSN 2162-237X. E-ISSN 2162-2388
Grant CEP: GA MŠMT ED1.1.00/02.0070
Institucionální podpora: RVO:67985807
Klíčová slova: associative memory * bars problem (BP) * Boolean factor analysis (BFA) * data mining * dimension reduction * Hebbian learning rule * information gain * likelihood maximization (LM) * neural network application * recurrent neural network * statistics
Obor OECD: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Impakt faktor: 6.108, rok: 2016

An usual task in large data set analysis is searching for an appropriate data representation in a space of fewer dimensions. One of the most efficient methods to solve this task is factor analysis. In this paper, we compare seven methods for Boolean factor analysis (BFA) in solving the so-called bars problem (BP), which is a BFA benchmark. The performance of the methods is evaluated by means of information gain. Study of the results obtained in solving BP of different levels of complexity has allowed us to reveal strengths and weaknesses of these methods. It is shown that the Likelihood maximization Attractor Neural Network with Increasing Activity (LANNIA) is the most efficient BFA method in solving BP in many cases. Efficacy of the LANNIA method is also shown, when applied to the real data from the Kyoto Encyclopedia of Genes and Genomes database, which contains full genome sequencing for 1368 organisms, and to text data set R52 (from Reuters 21578) typically used for label categorization.
Trvalý link: http://hdl.handle.net/11104/0212684