Name
Abstract | ||
|---|---|---|
This paper presents a Local Learning Projec- tion (LLP) approach for linear dimensional- ity reduction. We first point out that the well known Principal Component Analysis (PCA) essentially seeks the projection that has the minimal global estimation error. Then we propose a dimensionality reduction algorithm that leads to the projection with the mini- mal local estimation error, and elucidate its advantages for classification tasks. We also indicate that LLP keeps the local informa- tion in the sense that the projection value of each point can be well estimated based on its neighbors and their projection values. Exper- imental results are provided to validate the eectiveness of the proposed algorithm. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1145/1273496.1273627 | ICML |
Keywords | Field | DocType |
linear dimensionality reduction,classification task,local information,minimal global estimation error,dimensionality reduction algorithm,projection value,local learning projection,principal component analysis,proposed algorithm,minimal local estimation error | Dimensionality reduction,Projection pursuit,Local learning,Pattern recognition,Computer science,Artificial intelligence,Machine learning,Principal component analysis | Conference |
Citations | PageRank | References |
21 | 1.16 | 10 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mingrui Wu | 1 | 515 | 23.03 |
| Yu, Kai | 2 | 4799 | 255.21 |
| Shipeng Yu | 3 | 1767 | 118.84 |
| Bernhard Schölkopf | 4 | 23120 | 3091.82 |