Name
Abstract | ||
|---|---|---|
We present a local learning approach for clustering. The basic idea is that a good clustering result should have the property that the cluster label of each data point can be well predicted based on its neighboring data and their cluster labels, us- ing current supervised learning methods. An optimization problem is formulated such that its solution has the above property. Relaxation and eigen-decomposition are applied to solve this optimization problem. We also briefly investigate the pa- rameter selection issue and provide a simple parameter selection method for the proposed algorithm. Experimental results are provided to validate the effective- ness of the proposed approach. |
Year | Venue | Keywords |
|---|---|---|
2006 | NIPS | optimization problem,supervised learning |
Field | DocType | Citations |
Mathematical optimization,Semi-supervised learning,Local learning,Correlation clustering,Computer science,Supervised learning,Constrained clustering,Artificial intelligence,Cluster analysis,Optimization problem,Machine learning | Conference | 77 |
PageRank | References | Authors |
2.94 | 8 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| minyou wu | 1 | 77 | 2.94 |
| Bernhard Schölkopf | 2 | 23120 | 3091.82 |
| scholkopf | 3 | 216 | 13.73 |
| John Platt | 4 | 6611 | 1100.14 |
| Thomas Hofmann | 5 | 10064 | 1001.83 |