Learning using privileged information in prototype based models
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
Authors
Colleges, School and Institutes
External organisations
- Centre for Cardiovascular Sciences; School of Clinical and Experimental Medicine; University of Birmingham; Edgbaston Birmingham UK
Abstract
In some pattern analysis problems, there exists expert knowledge, in addition to the original data involved in the classification process. Most of existing approaches ignore such auxiliary (privileged) knowledge. Recently a new learning paradigm - Learning Using Hidden Information - was introduced in the SVM+ framework. This approach is formulated for binary classification and, as typical for many kernel based methods, can scale unfavorably with the number of training examples. In this contribution we present a more direct novel methodology, based on a prototype metric learning model, for incorporation of valuable privileged knowledge. This is done by changing the global metric in the input space, based on distance relations revealed by the privileged information. Our method achieves competitive performance against the SVM+ formulations. We also present a successful application of our method to a large scale multi-class real world problem of galaxy morphology classification.
Details
Original language | English |
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Title of host publication | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
Publication status | Published - 2012 |
Event | 22nd International Conference on Artificial Neural Networks, ICANN 2012 - Lausanne, Switzerland Duration: 11 Sep 2012 → 14 Sep 2012 |
Publication series
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Number | PART 2 |
Volume | 7553 LNCS |
ISSN (Print) | 03029743 |
ISSN (Electronic) | 16113349 |
Conference
Conference | 22nd International Conference on Artificial Neural Networks, ICANN 2012 |
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Country | Switzerland |
City | Lausanne |
Period | 11/09/12 → 14/09/12 |
Keywords
- Generalized Matrix Learning Vector Quantization (GMLVQ), Information Theoretic Metric Learning (ITML), Learning Using Hidden Information (LUHI)