Name
Title | ||
|---|---|---|
Passage method for nonlinear dimensionality reduction of data on multi-cluster manifolds |
Abstract | ||
|---|---|---|
Nonlinear dimensionality reduction of data lying on multi-cluster manifolds is a crucial issue in manifold learning research. An effective method, called the passage method, is proposed in this paper to alleviate the disconnectivity, short-circuit, and roughness problems ordinarily encountered by the existing methods. The specific characteristic of the proposed method is that it constructs a globally connected neighborhood graph superimposed on the data set through technically building the smooth passages between separate clusters, instead of supplementing some rough inter-cluster connections like some existing methods. The neighborhood graph so constructed is naturally configured as a smooth manifold, and hence complies with the effectiveness condition underlying manifold learning. This theoretical argument is supported by a series of experiments performed on the synthetic and real data sets residing on multi-cluster manifolds. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1016/j.patcog.2013.01.028 | Pattern Recognition |
Keywords | Field | DocType |
smooth manifold,manifold learning,existing method,nonlinear dimensionality reduction,effective method,multi-cluster manifold,smooth passage,neighborhood graph,passage method | Topology,Graph,Cluster (physics),Data set,Effective method,Manifold alignment,Artificial intelligence,Invariant manifold,Nonlinear dimensionality reduction,Mathematics,Manifold,Machine learning | Journal |
Volume | Issue | ISSN |
46 | 8 | 0031-3203 |
Citations | PageRank | References |
3 | 0.37 | 19 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Deyu Meng | 1 | 2025 | 105.31 |
| Yee Leung | 2 | 2081 | 96.44 |
| Zongben Xu | 3 | 3203 | 198.88 |