Name
Abstract | ||
|---|---|---|
The Fisher discriminant analysis (FDA) is a common technique for binary classification. A parametrized extension, which we call the extended FDA, has been introduced from the viewpoint of robust optimization. In this work, we first give a new probabilistic interpretation of the extended FDA. We then develop algorithms for solving an optimization problem that arises from the extended FDA: computing the distance between a point and the surface of an ellipsoid. We solve this problem via the KKT points, which we show are obtained by solving a generalized eigen-value problem. We speed up the algorithm by taking advantage of the matrix structure and proving that a globally optimal solution is a KKT point with the smallest Lagrange multiplier, which can be computed efficiently as the leftmost eigenvalue. Numerical experiments illustrate the efficiency and effectiveness of the extended FDA model combined with our algorithm. |
Year | Venue | Field |
|---|---|---|
2014 | JMLR Workshop and Conference Proceedings | Ellipsoid,Mathematical optimization,Global optimization,Computer science,Robust optimization,Lagrange multiplier,Artificial intelligence,Linear discriminant analysis,Karush–Kuhn–Tucker conditions,Optimization problem,Eigenvalues and eigenvectors,Machine learning |
DocType | Volume | ISSN |
Conference | 33 | 1938-7288 |
Citations | PageRank | References |
3 | 0.40 | 7 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Satoru Iwata | 1 | 759 | 70.03 |
| Yuji Nakatsukasa | 2 | 97 | 17.74 |
| Akiko Takeda | 3 | 196 | 29.72 |