Iterative Kernel Principal Component Analysis for Image Modeling
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast Iterative Kernel Principal Component Analysis
The Journal of Machine Learning Research
Fast principal component analysis based on hardware architecture of generalized Hebbian algorithm
ISICA'10 Proceedings of the 5th international conference on Advances in computation and intelligence
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This paper presents a novel hardware architecture based on generalized Hebbian algorithm (GHA) for texture classification. In the architecture, the weight vector updating process is separated into a number of stages for lowering area costs and increasing computational speed. Both the weight vector updating process and principle component computation process can also operate concurrently to further enhance the throughput. The proposed architecture has been embedded in a system-on-programmable-chip (SOPC) platform for physical performance measurement. Experimental results show that the proposed architecture is an efficient design for attaining both high speed performance and low area costs.