Name
Abstract | ||
|---|---|---|
Recently, there are several nonlinear dimensionality reduction algorithms that can discover the low-dimensional coordinates on a manifold based on training samples, such as ISOMAP, LLE, Laplacian eigenmaps. However, most of these algorithms work in batch mode. In this paper, we presented an incremental nonlinear dimensionality reduction algorithm to efficiently map new samples into the embedded space. The method permits one to select some landmark points and to only preserve geodesic distances between new data and landmark points. Self-organizing map algorithm is used to choose landmark points. Experiments demonstrate that the proposed algorithm is effective. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1007/11589990_104 | Australian Conference on Artificial Intelligence |
Keywords | Field | DocType |
new sample,incremental nonlinear dimensionality reduction,self-organizing map algorithm,batch mode,nonlinear dimensionality reduction algorithm,laplacian eigenmaps,algorithms work,new data,landmark point,proposed algorithm,geodesic distance,nonlinear dimensionality reduction | Dimensionality reduction,Landmark point,Computer science,Algorithm,Diffusion map,Nonlinear dimensionality reduction,Landmark,Geodesic,Difference-map algorithm,Isomap | Conference |
Volume | ISSN | ISBN |
3809 | 0302-9743 | 3-540-30462-2 |
Citations | PageRank | References |
5 | 0.44 | 2 |
Authors | ||
3 | ||