Proceedings Article10.1109/ROBIO.2004.1521908
Multivariate Pattern Classification based on Local Discriminant Component Analysis
Nan Bu,Toshio Tsuji +1 more
- 22 Aug 2004
- pp 924-929
TL;DR: A hybrid training algorithm is proposed on the basis of the minimum classification error (MCE) learning and a probabilistic neural network is developed based on the idea of local DCA, in which the whole network including the feature extractor and the classifier can be modulated according to a single training criterion.
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Abstract: This paper proposes a novel local discriminant component analysis (DCA) algorithm that is useful for pattern classification of high-dimensional data Different from most traditional methods, in which feature extractors are usually used prior to a classifier, the proposed method incorporates the feature extraction process into the classifier Then, a probabilistic neural network is developed based on the idea of local DCA, in which the whole network including the feature extractor and the classifier can be modulated according to a single training criterion, so that features suited to the classification purpose can be extracted In this paper, a hybrid training algorithm is proposed on the basis of the minimum classification error (MCE) learning In simulation experiments, benchmark data are used to prove feasibility of the proposed method
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Citations
Phoneme Classification for Speech Synthesiser using Differential EMG Signals between Muscles
Nan Bu,Toshio Tsuji,Jun Arita,M. Ohga +3 more
- 01 Jan 2005
TL;DR: Experimental results show that the proposed method can achieve considerably high classification performance with fewer electrodes.
A novel pattern classification method for multivariate EMG signals using neural network
Nan Bu,Jun Arita,Toshio Tsuji +2 more
- 27 Aug 2005
TL;DR: This paper proposes a novel classification method to deal with high-dimensional EMG patterns, using a probabilistic neural network, a reduced-dimensional log-linearized Gaussian mixture network (RD-LLGMN)
Selection of Motor Imageries for Brain-Computer Interfaces Based on Partial Kullback-Leibler Information Measure
Taro Shibanoki,Yuki Koizumi,Bi Adriel Yozan,Toshio Tsuji +3 more
- 10 Dec 2018
TL;DR: In the experiments performed, various motor imageries were learned by the reduced-dimensional recurrent probabilistic neural network and quasi-optimal combinations were selected using the proposed method.
2
A Recurrent Probabilistic Neural Network with Dimensional Reduction and Its Application to Time Series EEG Discrmination
TL;DR: A novel reduced-dimensional recurrent probabilistic neural network is proposed, and it is tried to classify electroencephalography (EEG) during motor images and indicated that the method has possibility to be applied for the human-machine interfaces.
時系列判別成分分析に基づく次元圧縮型リカレント確率ニューラルネット;時系列判別成分分析に基づく次元圧縮型リカレント確率ニューラルネット;A Reduced-dimensional Recurrent Probabilistic Neural Network Based on Time-series Discriminant Component Analysis
TL;DR: 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 and to reduce the computation time taken for network training.
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