Name
Title | ||
|---|---|---|
A Model of Selecting the Parameters Based on the Variance of Distance Ratios for Manifold Learning Algorithms |
Abstract | ||
|---|---|---|
ISOMAP, LLE, Laplacian Eigenmaps and LTSA are several representative manifold learning algorithms. In most of manifold learning methods, there are two free parameters: the neighborhood size and the intrinsic dimension of the high dimensional data set. In this paper, we analyze and compare the stress function, the residual variance and the dy-dx representation. On the basis of the dy-dx representation, a quantitative measure based on the variance of distance ratios is used to determine these two parameters, which overcomes faults of the stress function and the residual variance. Experiments show that the model can be utilized not only to choose an appropriate neighborhood size but also to estimate the intrinsic dimension of the high dimensional complex data for different manifold learning techniques. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/FSKD.2009.471 | FSKD (2) |
Keywords | Field | DocType |
representative manifold,neighborhood size,distance ratio,intrinsic dimension,appropriate neighborhood size,stress function,high dimensional complex data,residual variance,dy-dx representation,different manifold,manifold learning algorithms,high dimensional data,distance ratios,learning artificial intelligence,complex data,data models,manifolds,manifold learning,stress,data mining | Manifold alignment,Intrinsic dimension,Artificial intelligence,Nonlinear dimensionality reduction,Manifold,Residual,Clustering high-dimensional data,Pattern recognition,Algorithm,Machine learning,Mathematics,Isomap,Free parameter | Conference |
Citations | PageRank | References |
1 | 0.35 | 10 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lukui Shi | 1 | 11 | 4.33 |
| Qingxin Yang | 2 | 4 | 2.84 |
| Yong Xu | 3 | 9 | 9.53 |
| Pilian He | 4 | 29 | 7.46 |