Class-specific kernel discriminant analysis based on Cholesky decomposition
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review
Details
| Original language | English |
|---|---|
| Title of host publication | 2017 International Joint Conference on Neural Networks, IJCNN 2017 |
| Publisher | IEEE |
| Pages | 1141-1146 |
| Number of pages | 6 |
| ISBN (Electronic) | 9781509061815 |
| DOIs | |
| Publication status | Published - 30 Jun 2017 |
| Publication type | A4 Article in a conference publication |
| Event | International Joint Conference on Neural Networks - Duration: 1 Jan 1900 → … |
Publication series
| Name | |
|---|---|
| ISSN (Electronic) | 2161-4407 |
Conference
| Conference | International Joint Conference on Neural Networks |
|---|---|
| Period | 1/01/00 → … |
Abstract
In this paper we describe a method for nonlinear class-specific discriminant learning that is based on Cholesky Decomposition. We show that the optimization problem solved in Class-Specific Kernel Discriminant Analysis is equivalent to that of Low-Rank Kernel Regression using training data independent target vectors. This connection allows us to devise a new Class-Specific Kernel Discriminant Analysis method that can be trained by exploiting fast linear system approaches, like the Cholesky decomposition. We verify our analysis in publicly available verification problems designed for human action recognition.