Extracting highly effective features for supervised learning via simultaneous tensor factorization

Publication Type:
Conference Proceeding
Citation:
31st AAAI Conference on Artificial Intelligence, AAAI 2017, 2017, pp. 4995 - 4996
Issue Date:
2017-01-01
Metrics:
Full metadata record
Files in This Item:
Filename Description Size
14342-66723-1-PB.pdfPublished version702.7 kB
Adobe PDF
Copyright © 2017, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. Real world data is usually generated over multiple time periods associated with multiple labels, which can be represented as multiple labeled tensor sequences. These sequences are linked together, sharing some common features while exhibiting their own unique features. Conventional tensor factorization techniques are limited to extract either common or unique features, but not both simultaneously. However, both types of these features are important in many machine learning systems as they inherently affect the systems' performance. In this paper, we propose a novel supervised tensor factorization technique which simultaneously extracts ordered common and unique features. Classification results using features extracted by our method on CIFAR-10 database achieves significantly better performance over other factorization methods, illustrating the effectiveness of the proposed technique.
Please use this identifier to cite or link to this item: