NDLI: Adapting Component Analysis

Content Provider	IEEE Xplore Digital Library
Author	Dorri, F. Ghodsi, A.
Copyright Year	2012
Abstract	A main problem in machine learning is to predict the response variables of a test set given the training data and its corresponding response variables. A predictive model can perform satisfactorily only if the training data is an appropriate representative of the test data. This is usually reï¬ected in the assumption that the training data and the test data are drawn from the same underlying probability distribution. However, the assumption may not be correct in many applications for various reasons. We propose a method based on kernel distribution embedding and Hilbert-Schmidt Independence Criterion (HSIC) to address this problem. The proposed method explores a new representation of the data in a new feature space with two properties: (i) the distributions of the training and the test data sets are as close as possible in the new feature space, and (ii) the important structural information of the data is preserved. The algorithm can reduce the dimensionality of the data while it preserves the aforementioned properties and therefore it can be seen as a dimensionality reduction method as well. Our method has a closed-form solution and the experimental results show that it works well in practice.
Starting Page	846
Ending Page	851
File Size	345611
Page Count	6
File Format	PDF
ISBN	9781467346498
ISSN	15504786
DOI	10.1109/ICDM.2012.85
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-12-10
Publisher Place	Belgium
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Training data Probability distribution Kernel Error analysis Algorithm design and analysis Predictive models Hilbert- Schmidt independence criterion Domain Adaptation Kernel Embedding
Content Type	Text
Resource Type	Article

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3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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