Dimensionality Reduction in Unsupervised Learning of Conditional Gaussian Networks
IEEE Transactions on Pattern Analysis and Machine Intelligence
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WI '01 Proceedings of the First Asia-Pacific Conference on Web Intelligence: Research and Development
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PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
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FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 1
ICCSA'07 Proceedings of the 2007 international conference on Computational science and its applications - Volume Part I
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KES'10 Proceedings of the 14th international conference on Knowledge-based and intelligent information and engineering systems: Part II
Investigating a novel GA-based feature selection method using improved KNN classifiers
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IDEAL'11 Proceedings of the 12th international conference on Intelligent data engineering and automated learning
Assessment of an unsupervised feature selection method for generative topographic mapping
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part II
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Advances in Artificial Intelligence
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Dimensionality reduction is an important problem for efficient handling of large databases. Many feature selection methods exist for supervised data having class information. Little work has been done for dimensionality reduction of unsupervised data in which class information is not available. Principal Component Analysis (PCA) is often used. However, PCA creates new features. It is difficult to obtain intuitive understanding of the data using the new features only. In this paper we are concerned with the problem of determining and choosing the important original features for unsupervised data. Our method is based on the observation that removing an irrelevant feature from the feature set may not change the underlying concept of the data, but not so otherwise. We propose an entropy measure for ranking features, and conduct extensive experiments to show that our method is able to find the important features. Also it compares well with a similar feature ranking method (Relief) that requires class information unlike our method.