Name
Abstract | ||
|---|---|---|
Isometric feature mapping (Isomap) is a promising manifold learning method.
However, Isomap fails to work on data which distribute on clusters in a single
manifold or manifolds. Many works have been done on extending Isomap to
multi-manifolds learning. In this paper, we first proposed a new
multi-manifolds learning algorithm (M-Isomap) with help of a general procedure.
The new algorithm preserves intra-manifold geodesics and multiple
inter-manifolds edges precisely. Compared with previous methods, this algorithm
can isometrically learn data distributed on several manifolds. Secondly, the
original multi-cluster manifold learning algorithm first proposed in
\cite{DCIsomap} and called D-C Isomap has been revised so that the revised D-C
Isomap can learn multi-manifolds data. Finally, the features and effectiveness
of the proposed multi-manifolds learning algorithms are demonstrated and
compared through experiments. |
Year | Venue | Keywords |
|---|---|---|
2009 | Clinical Orthopaedics and Related Research | pattern recognition,manifold learning |
Field | DocType | Volume |
Semi-supervised learning,Manifold alignment,Isometric feature mapping,Artificial intelligence,Nonlinear dimensionality reduction,Manifold,Geodesic,Mathematics,Machine learning,Isomap | Journal | abs/0912.0 |
Citations | PageRank | References |
0 | 0.34 | 11 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mingyu Fan | 1 | 34 | 5.60 |
| Hong Qiao | 2 | 1147 | 110.95 |
| Bo Zhang | 3 | 253 | 21.61 |