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ID 113538
Author
Takeuchi, Yohei The University of Tokushima
Keywords
Pattern recognition
principal component analysis
supervised learning
Content Type
Journal Article
Description
In the field of pattern recognition, principal component analysis (PCA) is one of the most well-known feature extraction methods for reducing the dimensionality of high-dimensional datasets. Simple-PCA (SPCA), which is a faster version of PCA, performs effectively with iterative operated learning. However, SPCA might not be efficient when input data are distributed in a complex manner because it learns without using the class information in the dataset. Thus, SPCA cannot be said to be optimal from the perspective of feature extraction for classification. In this study, we propose a new learning algorithm that uses the class information in the dataset. Eigenvectors spanning the eigenspace of the dataset are produced by calculating the data variations within each class. We present our proposed algorithm and discuss the results of our experiments that used UCI datasets to compare SPCA and our proposed algorithm.
Journal Title
International Journal of Machine Learning and Computing
ISSN
20103700
Volume
2
Issue
5
Start Page
720
End Page
724
Published Date
2012-10
Rights
This article is licensed under an open access Creative Commons CC BY 4.0 license(https://creativecommons.org/licenses/by/4.0/), which means all papers can be downloaded, shared, and reused without restriction, as long as the original authors are properly cited.
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DOI (Published Version)
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language
eng
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departments
Science and Technology