---
title: "Improving Embeddings by Flexible Exploitation of Side Information"
authors:
  - "Ali Ghodsi"
  - "Dana F. Wilkinson"
  - "Finnegan Southey"
doi: ""
url: "https://ijcai.org/proceedings/2007/130"
pdf: "https://ijcai.org/proceedings/2007/0130.pdf"
venue: "Proceedings of the Twentieth International Joint Conference on Artificial Intelligence"
year: 2007
pages: "810-815"
track: ""
keywords:
---

# Improving Embeddings by Flexible Exploitation of Side Information

Ali Ghodsi, Dana F. Wilkinson, Finnegan Southey

*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.