Name
Abstract | ||
|---|---|---|
Visualization and clustering of multivariate data are usually based on mutual distances of samples, measured by heuristic means such as the Euclidean distance of vectors of extracted features. Our recently developed methods remove this arbitrariness by learning to measure important differences. The effect is equivalent to changing the metric of the data space. It is assumed that variation of the data is important only to the extent it causes variation in auxiliary data which is available paired to the primary data. The learning of the metric is supervised by the auxiliary data, whereas the data analysis in the new metric is unsupervised. We review two approaches: a clustering algorithm and another that is based on an explicitly generated metric. Applications have so far been in exploratory analysis of texts, gene function, and bankruptcy. Relationships of the two approaches are derived, which leads to new promising approaches to the clustering problem. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1023/B:VLSI.0000027483.39774.f8 | Journal of VLSI Signal Processing Systems |
Keywords | Field | DocType |
discriminative clustering,exploratory data analysis,Fisher information matrix,information metric,Hebbian learning metrics | Hierarchical clustering,k-medians clustering,Data mining,Clustering high-dimensional data,Pattern recognition,Computer science,Euclidean distance,Fisher information,Artificial intelligence,Conceptual clustering,Exploratory data analysis,Cluster analysis | Journal |
Volume | Issue | ISSN |
37 | 2/3 | 0922-5773 |
Citations | PageRank | References |
12 | 1.19 | 7 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Samuel Kaski | 1 | 2755 | 245.52 |
| Janne Sinkkonen | 2 | 231 | 21.36 |