title: A Recurrent Probabilistic Neural Network with Dimensionality Reduction Based on Time-series Discriminant Component Analysis.
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.
authors.forename: Hideaki
authors.initials: H
authors.lastname: Hayashi
authors.forename: Taro
authors.initials: T
authors.lastname: Shibanoki
authors.forename: Keisuke
authors.initials: K
authors.lastname: Shima
authors.forename: Yuichi
authors.initials: Y
authors.lastname: Kurita
authors.forename: Toshio
authors.initials: T
authors.lastname: Tsuji
mesh_terms: D000465:Algorithms; D016002:Discriminant Analysis; D006801:Humans; D015233:Models, Statistical; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated; D011336:Probability; D013997:Time Factors