Learning sparse metrics via linear programming

Calculation of object similarity, for example through a distance function, is a common part of data mining and machine learning algorithms. This calculation is crucial for efficiency since distances are usually evaluated a large number of times, the classical example being query-by-example (find objects that are similar to a given query object). Moreover, the performance of these algorithms depends critically on choosing a good distance function. However, it is often the case that (1) the correct distance is unknown or chosen by hand, and (2) its calculation is computationally expensive (e.g., such as for large dimensional objects). In this paper, we propose a method for constructing relative-distance preserving low-dimensional mappings (sparse mappings) to allow learning unknown distance functions or approximating known functions, with the additional property of reducing distance computation time. We present an algorithm that given examples of proximity comparisons among triples of o...

• Type: Conference
• Year: 2006
• Where: KDD
• Authors: Glenn Fung, Rómer Rosales
• Keywords: Data Mining, Distance Computation Time, Distance Function, KDD 2006, Unknown Distance Functions