Name
Abstract | ||
|---|---|---|
Popular nonlinear dimensionality reduction algorithms, e.g., LLE, Isomap and SIE suffer a difficulty in common: neighborhood parameter has to be configured in advance to gain meaningful embedding results. Simulation shows that embedding often loses relevance under improper parameters configures. But current embedding residual criterions of neighborhood parameters selection are not independent to neighborhood parameters. Therefore it cannot work universally. To improve the availability of nonlinear dimensionality reduction algorithms in the field of self-adaptive machine learning, it is necessary to find some transcendent criterions to achieve unsupervised parameters selection. This paper begins with a discussion of optimal embedding principles and proposes a statistics based on spatial mutual information and normalized dependency index spectrum to determine reasonable parameters configuration. The simulation supports our proposal effectively. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1007/11881599_47 | FSKD |
Keywords | Field | DocType |
meaningful embedding result,current embedding residual criterion,neighborhood parameter,nonlinear dimensionality reduction algorithm,unsupervised parameters selection,neighborhood parameters selection,statistical criterion,self-organizing isometric,popular nonlinear dimensionality reduction,reasonable parameters configuration,improper parameters configures,optimal embedding principle,machine learning,mutual information,self organization,nonlinear dimensionality reduction,indexation,spectrum | Residual,Dimensionality reduction,Embedding,Normalization (statistics),Computer science,Fuzzy logic,Mutual information,Artificial intelligence,Nonlinear dimensionality reduction,Machine learning,Isomap | Conference |
Volume | ISSN | ISBN |
4223 | 0302-9743 | 3-540-45916-2 |
Citations | PageRank | References |
0 | 0.34 | 4 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ruiguo Yu | 1 | 9 | 12.96 |
| Yuexian Hou | 2 | 269 | 38.59 |
| Pilian He | 3 | 29 | 7.46 |