By Topic

α-EM algorithm and α-ICA learning based upon extended logarithmic information measures

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

5 Author(s)
Mtsuyama, Y. ; Dept. of Electr. Electron. & Comput. Eng., Waseda Univ., Tokyo, Japan ; Nimoto, T. ; Katsumata, N. ; Suzuki, Y.
more authors

The α-logarithm extends the logarithm as the special case of α=-1. Usage of α-related information measures based upon this extended logarithm is expected to be effective to speedup of convergence, i.e., on the improvement of learning aptitude. In this paper, two typical cases are investigated. One is the α-EM algorithm (α-expectation-maximization algorithm) which is derived from the α-log-likelihood ratio. The other is the α-ICA (α-independent component analysis) which is formulated as minimizing the α-mutual information. In the derivation of both algorithms, the α-divergence plays the main role. For the α-EM algorithm, the reason for the speedup is explained using Hessian and Jacobian matrices for learning. For the α-ICA learning, methods of exploiting the past and future information are presented. Examples are shown on single-loop α-EM and sample-based α-ICA. In all cases, effective speedups are observed. Thus, this paper's examples together with formerly reported ones are evidences that the speed improvement by the α-logarithm is a general property beyond individual problems

Published in:

Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on  (Volume:3 )

Date of Conference:

2000