Name
Abstract | ||
|---|---|---|
Discriminative dimensionality reduction aims at a low dimensional, usually nonlinear representation of given data such that information as specified by auxiliary discriminative labeling is presented as accurately as possible. This paper centers around two open problems connected to this question: (i) how to evaluate discriminative dimensionality reduction quantitatively? (ii) how to arrive at explicit nonlinear discriminative dimensionality reduction mappings? Based on recent work for the unsupervised case, we propose an evaluation measure and an explicit discriminative dimensionality reduction mapping using the Fisher information. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/978-3-642-34156-4_13 | IDA |
Keywords | Field | DocType |
low dimensional,discriminative dimensionality reduction,evaluation measure,discriminative dimensionality reduction quantitatively,nonlinear representation,discriminative dimensionality reduction mapping,reduction mapping,auxiliary discriminative,explicit nonlinear discriminative dimensionality,explicit discriminative dimensionality reduction,fisher information | Dimensionality reduction,Nonlinear system,Pattern recognition,Computer science,Artificial intelligence,Fisher information,Linear discriminant analysis,Discriminative model,Machine learning | Conference |
Citations | PageRank | References |
5 | 0.43 | 15 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Andrej Gisbrecht | 1 | 195 | 15.60 |
| Daniela Hofmann | 2 | 54 | 3.30 |
| Barbara Hammer | 3 | 2383 | 181.34 |