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

TL;DR

This paper introduces a novel neural network model that combines time-series discriminant component analysis with probabilistic classification, enabling efficient and accurate high-dimensional time-series pattern recognition.

Contribution

It presents a new recurrent neural network framework that integrates dimensionality reduction and classification into a single trainable model using backpropagation through time.

Findings

Effective classification of high-dimensional artificial data

Successful application to EEG signal analysis

Reduced training computation time

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.