Feature Extraction, also known as Multidimensional Scaling, is a basic primitive associated with indexing, clustering, nearest neighbor searching and visualization. We consider the problem of feature extraction when the data-points are complex and the distance evaluation function is very expensive to evaluate. Examples of expensive distance evaluations include those for computing the Hausdorff distance between polygons in a spatial database, or the edit distance between macromolecules in a DNA or protein database. We propose Cofe, a method for sparse feature extraction which is based on novel random non-linear projections. We evaluate Cofe on real data and find that it performs very well in terms of quality of features extracted, number of distances evaluated, number of database scans performed and total run time.We further propose Cofe-GR, which matches Cofe in terms of distance evaluations and run-time, but outperforms it in terms of quality of features extracted.
|Original language||English (US)|
|Number of pages||14|
|Journal||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|State||Published - 2000|
All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computer Science(all)