{"type":"ScholarlyArticle","title":"Improving Embeddings by Flexible Exploitation of Side Information","authors":[{"name":"Ali Ghodsi","slug":"ali-ghodsi","given":"Ali","family":"Ghodsi","affiliation":""},{"name":"Dana F. Wilkinson","slug":"dana-f-wilkinson","given":"Dana F.","family":"Wilkinson","affiliation":""},{"name":"Finnegan Southey","slug":"finnegan-southey","given":"Finnegan","family":"Southey","affiliation":""}],"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.","doi":null,"url":"https://ijcai.org/proceedings/2007/130","pdf":"https://ijcai.org/proceedings/2007/0130.pdf","markdown":"https://ijcai.org/proceedings/2007/130.md","bibtex":"https://ijcai.org/proceedings/2007/bibtex/130","volume":{"slug":"2007","title":"Proceedings of the Twentieth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2007","url":"https://ijcai.org/proceedings/2007/","year":2007,"location":"Hyderabad, India","dates":"","editors":"Ramon López de Mántaras","isbn":"","publisher":"International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":null,"track":"","number":130,"pages":{"first":810,"last":815},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}