Abstract
We have proposed an online feature extraction method called Chunk Incremental Principal Component Analysis (Chunk IPCA) where a chunk of data is trained at a time to update an eigenspace model. In this paper, we propose an extended version of Chunk IPCA in which a proper threshold for the accumulation ratio is adaptively determined such that the highest classification accuracy is maintained for a validation data set. Whenever a new chunk of training data is given, the validation set is updated in an online fashion by using the k-means clustering or through the prototype selection based on the classification results. The experimental results show that the extended version of Chunk IPCA can determine a proper threshold on an ongoing basis, resulting in keeping higher classification accuracy than the original Chunk IPCA.
The authors would like to thank Professor Shigeo Abe for his helpful comments and discussions. This research was partially supported by the Ministry of Education, Science, Sports and Culture, Grant-in-Aid for Scientific Research (C) 205002205.
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Ozawa, S., Matsumoto, K., Pang, S., Kasabov, N. (2009). Incremental Principal Component Analysis Based on Adaptive Accumulation Ratio. In: Köppen, M., Kasabov, N., Coghill, G. (eds) Advances in Neuro-Information Processing. ICONIP 2008. Lecture Notes in Computer Science, vol 5506. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02490-0_145
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DOI: https://doi.org/10.1007/978-3-642-02490-0_145
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