Recently the Isometric mapping (Isomap) method has demonstrated promising results in finding low dimensional manifolds from data points in the high dimensional input space. While classical subspace methods use Euclidean or Manhattan metrics to represent distances between data points and apply Principal Component Analysis to induce linear manifolds, the Isomap method estimates geodesic distances between data points and then uses Multi-Dimensional Scaling to induce low dimensional manifolds. Since the Isomap method is developed based on reconstruction principle, it may not be optimal from the classification viewpoint. In this paper, we present a discriminant isometric method that utilizes Fisher Linear Discriminant for pattern classification. Numerous experiments on three image databases show that our extension is more effective than the original Isomap method for pattern classification. Furthermore, the proposed method shows promising results compared with best methods in the face recognition literature.
|Number of pages||11|
|Journal||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Publication status||Published - 2003 Dec 1|
All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computer Science(all)