A Recurrent Probabilistic Neural Network with Dimensionality Reduction Based on Time-series Discriminant Component Analysis

Abstract

This paper proposes a probabilistic neural network 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 a neural network, 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 EEG signals in the experiments conducted during the study.

Published in IEEE Transactions on Neural Networks and Learning Systems

References in corpus (1)

• Time Series Classification by Class-Specific Mahalanobis Distance Measures

Cited by in corpus (3)

• Adaptive Transfer Learning of Multi-View Time Series Classification
• A Neural Network Based on the Johnson $S_{\mathrm{U}}$ Translation System and Related Application to Electromyogram Classification
• A Trainable Multiplication Layer for Auto-correlation and Co-occurrence Extraction