Proceedings

Improving Embeddings by Flexible Exploitation of Side Information

Ali Ghodsi, Dana F. Wilkinson, Finnegan Southey

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Proceedings of the Twentieth International Joint Conference on Artificial Intelligence. Pages 810-815.

Abstract

Dimensionality reduction is a much-studied task in machine learning in which high-dimensional data is mapped, possibly via a non-linear transformation, onto a low-dimensional manifold. The resulting embeddings, however, may fail to capture features of interest. One solution is to learn a distance metric which prefers embeddings that capture the salient features. We propose a novel approach to learning a metric from side information to guide the embedding process. Our approach admits the use of two kinds of side information. The first kind is class-equivalence information, where some limited number of pairwise same/different class statements are known. The second form of side information is a limited set of distances between pairs of points in the target metric space. We demonstrate the effectiveness of the method by producing embeddings that capture features of interest.

Ali Ghodsi, Dana F. Wilkinson, and Finnegan Southey. 2007. Improving Embeddings by Flexible Exploitation of Side Information. In Proceedings of the Twentieth International Joint Conference on Artificial Intelligence, IJCAI-07. IJCAI, 810–815. https://ijcai.org/proceedings/2007/130
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