Name
Abstract | ||
|---|---|---|
Analysis of large amount of data is needed in many areas of science, and this depends on dimensionality reduction of the multivariate data. Local linear embedding (LLE) is efficient for many nonlinear dimension reduction problems because of its low computation complexity and high efficiency, however LLE often leads to invalidation in the event that the data is sparse or noise contaminated. In order to improve the ability of LLE to deal with the sparse and noise data, small world neighborhood optimized LLE algorithm (SLLE) is proposed based on the complex networks theory in the paper. The local parameters of SLLE are optimized by using the shortest path and the local neighbor set clustering coefficient. As a result, the problem of embedding distortion using locally linear patch of the manifold only defining neighborhood in Euclidean space is efficiently solved. The results of standard experiments show that SLLE algorithm makes LLE more robust against no-ideal data. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/CSSE.2008.723 | CSSE (4) |
Keywords | Field | DocType |
shortest path,dimension reduction,algorithm design and analysis,dimensionality reduction,graph theory,euclidean space,complex networks,clustering algorithms,computational complexity,manifolds,clustering coefficient,complex network,noise measurement,data analysis,euclidean distance,multivariate data,noise | Algorithm design,Dimensionality reduction,Embedding,Pattern recognition,Shortest path problem,Computer science,Euclidean distance,Artificial intelligence,Cluster analysis,Clustering coefficient,Machine learning,Computational complexity theory | Conference |
Volume | Issue | Citations |
4 | null | 3 |
PageRank | References | Authors |
0.45 | 3 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yulin Zhang | 1 | 3 | 0.79 |
| Jian Zhuang | 2 | 104 | 15.09 |
| Sun'an Wang | 3 | 34 | 5.40 |
| Xiaohu Li | 4 | 5 | 1.34 |