Name
Abstract | ||
|---|---|---|
A new distance measure between probability density functions (pdfs) is introduced, which we refer to as the Laplacian pdf dis- tance. The Laplacian pdf distance exhibits a remarkable connec- tion to Mercer kernel based learning theory via the Parzen window technique for density estimation. In a kernel feature space dened by the eigenspectrum of the Laplacian data matrix, this pdf dis- tance is shown to measure the cosine of the angle between cluster mean vectors. The Laplacian data matrix, and hence its eigenspec- trum, can be obtained automatically based on the data at hand, by optimal Parzen window selection. We show that the Laplacian pdf distance has an interesting interpretation as a risk function connected to the probability of error. |
Year | Venue | Keywords |
|---|---|---|
2004 | NIPS | feature space,probability of error,learning theory,cost function,density estimation,probability density function |
Field | DocType | Citations |
Density estimation,Laplacian matrix,Total variation distance of probability measures,Artificial intelligence,Cluster analysis,Probability density function,Mathematics,Machine learning,Kernel (statistics),Laplace operator,Kernel density estimation | Conference | 26 |
PageRank | References | Authors |
2.31 | 4 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Robert Jenssen | 1 | 370 | 43.06 |
| Deniz Erdogmus | 2 | 1299 | 169.92 |
| José Carlos Príncipe | 3 | 841 | 102.43 |
| Torbjørn Eltoft | 4 | 583 | 48.56 |