Name
Abstract | ||
|---|---|---|
Methods for nonlinear dimensionality reduction have been widely used for different purposes, but they are constrained to single manifold datasets. Considering that in real world applications, like video and image analysis, datasets with multiple manifolds are common, we propose a framework to find a low-dimensional embedding for data lying on multiple manifolds. Our approach is inspired on the manifold learning algorithm Laplacian Eigenmaps - LEM, computing the relationships among samples of different datasets based on an intra manifold comparison to unfold properly the data underlying structure. According to the results, our approach shows meaningful embeddings that outperform the results obtained by the conventional LEM algorithm and a previous close related work that analyzes multiple manifolds. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1007/978-3-642-25085-9_24 | CIARP |
Keywords | Field | DocType |
meaningful embeddings,intra manifold comparison,low-dimensional embedding,single manifold datasets,nonlinear dimensionality reduction,different datasets,algorithm laplacian eigenmaps,multiple manifold,image analysis,different purpose,conventional lem algorithm | Embedding,Pattern recognition,Computer science,Theoretical computer science,Manifold alignment,Artificial intelligence,Nonlinear dimensionality reduction,Manifold,Machine learning,Laplace operator | Conference |
Volume | ISSN | Citations |
7042 | 0302-9743 | 4 |
PageRank | References | Authors |
0.42 | 2 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Juliana Valencia-Aguirre | 1 | 35 | 3.03 |
| Andrés Álvarez-Meza | 2 | 25 | 2.46 |
| Genaro Daza-Santacoloma | 3 | 72 | 6.63 |
| Carlos Daniel Acosta-Medina | 4 | 20 | 4.70 |
| César Germán Castellanos-Domínguez | 5 | 55 | 9.51 |