Conditionally Independent Component Extraction for Naive Bayes Inference

Authors:
Shotaro Akaho
Affiliations:
-
Venue:
ICANN '01 Proceedings of the International Conference on Artificial Neural Networks
Year:
2001

Citing 5
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Abstract

This paper extends the framework of independent component analysis (ICA) to supervised learning. The key idea is to find a conditionally independent representation of input variables for given output. The representation is useful for the naive Bayes learning which has been reported to perform as well as more sophisticated methods. The learning algorithm is derived in a similar criterion to ICA. Two dimensional entropy takes an important role, while one dimensional entropy does in ICA.