Selective Dynamic Principal Component Analysis Using Recurrent Neural Networks
- Resource Type
- Conference
- Authors
- Hosseini, M. Noori; Gharibzadeh, S.; Gifani, P.; Babaei, S.; Makki, B.
- Source
- 2008 Fourth International Conference on Natural Computation Natural Computation, 2008. ICNC '08. Fourth International Conference on. 3:306-310 Oct, 2008
- Subject
- Computing and Processing
Principal component analysis
Recurrent neural networks
Basal ganglia
Data mining
Neural networks
Information processing
Databases
Speech analysis
Multidimensional signal processing
Biomedical signal processing
Bio-inspired system
recurrent neural networks
Principal Component Analysis
- Language
- ISSN
- 2157-9555
2157-9563
In the last decades, considerable attention has been focused on development of bio-inspired systems. This paper employs the principals of information processing in the Basal Ganglia (BG) to develop a new method for selectively extracting Dynamic Principal Components (DPCs) of multidimensional datasets. The DPCs are extracted by are current structure of auto-associative neural network and selectivity is achieved by means of a reinforcement-like signal which modifies the desired outputs and the learning coefficient of the network. Performance of the model is evaluated through two experiments; at first, the DPCs of a stock price database are extracted and then, speech compression capability of the method is checked which illustrates the efficiency of the proposed approach.