A recurrent probabilistic neural network with dimensionality reduction based on time-series discriminant component analysis

Hideaki Hayashi, Taro Shibanoki, Keisuke Shima, Yuichi Kurita, Toshio Tsuji

Research output: Contribution to journalArticle

16 Citations (Scopus)

Abstract

This paper proposes a probabilistic neural network (NN) developed on the basis of time-series discriminant component analysis (TSDCA) that can be used to classify high-dimensional time-series patterns. TSDCA involves the compression of high-dimensional time series into a lower dimensional space using a set of orthogonal transformations and the calculation of posterior probabilities based on a continuous-density hidden Markov model with a Gaussian mixture model expressed in the reduced-dimensional space. The analysis can be incorporated into an NN, which is named a time-series discriminant component network (TSDCN), so that parameters of dimensionality reduction and classification can be obtained simultaneously as network coefficients according to a backpropagation through time-based learning algorithm with the Lagrange multiplier method. The TSDCN is considered to enable high-accuracy classification of high-dimensional time-series patterns and to reduce the computation time taken for network training. The validity of the TSDCN is demonstrated for high-dimensional artificial data and electroencephalogram signals in the experiments conducted during the study.

Original languageEnglish
Article number7045517
Pages (from-to)3021-3033
Number of pages13
JournalIEEE Transactions on Neural Networks and Learning Systems
Volume26
Issue number12
DOIs
Publication statusPublished - Dec 1 2015
Externally publishedYes

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All Science Journal Classification (ASJC) codes

  • Software
  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence

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