Structure regularized unsupervised discriminant feature analysis

Publication Type:
Conference Proceeding
Citation:
31st AAAI Conference on Artificial Intelligence, AAAI 2017, 2017, pp. 1870 - 1876
Issue Date:
2017-01-01
Full metadata record
Files in This Item:
Filename Description Size
Structure Regularized Unsupervised Discriminant Feature Analysis.pdfPublished version732.33 kB
Adobe PDF
Copyright © 2017, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. Feature selection is an important technique in machine learning research. An effective and robust feature selection method is desired to simultaneously identify the informative features and eliminate the noisy ones of data. In this paper, we consider the unsupervised feature selection problem which is particularly difficult as there is not any class labels that would guide the search for relevant features. To solve this, we propose a novel algorithmic framework which performs unsupervised feature selection. Firstly, the proposed framework implements structure learning, where the data structures (including intrinsic distribution structure and the data segment) are found via a combination of the alternative optimization and clustering. Then, both the intrinsic data structure and data segmentation are formulated as regularization terms for discriminant feature selection. The results of the feature selection also affect the structure learning step in the following iterations. By leveraging the interactions between structure learning and feature selection, we are able to capture more accurate structure of data and select more informative features. Clustering and classification experiments on real world image data sets demonstrate the effectiveness of our method.
Please use this identifier to cite or link to this item: