A Recurrent Probabilistic Neural Network with Dimensionality Reduction Based on Time-series Discriminant Component Analysis
This work addresses the challenge of efficient and accurate classification of high-dimensional time-series data, such as EEG signals, for researchers in machine learning and signal processing, though it appears incremental as it builds on existing methods like hidden Markov models and neural networks.
The paper tackles the problem of classifying high-dimensional time-series patterns by proposing a probabilistic neural network based on time-series discriminant component analysis (TSDCA), which reduces dimensionality and computes posterior probabilities, achieving high accuracy and reduced computation time in experiments with artificial data and EEG signals.
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.