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Spatial Distance Preservation based Methods for Non-Linear Dimensionality Reduction
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 69 - Number 20 |
| Year of Publication: 2013 |
| Authors: Rashmi Gupta, Pooja Pandey, Rajiv Kapoor |
10.5120/12090-8281
|
Rashmi Gupta, Pooja Pandey, Rajiv Kapoor . Spatial Distance Preservation based Methods for Non-Linear Dimensionality Reduction. International Journal of Computer Applications. 69, 20 ( May 2013), 37-41. DOI=10.5120/12090-8281
Abstract
The preservation of the pairwise distances measured in a data set ensures that the low dimensional embedding inherits the main geometric properties of the data like the local neighborhood relationships. In this paper, distance preserving technique namely, Sammons nonlinear mapping (Sammon's NLM) and Curvilinear Component Analysis (CCA) have been discussed and compared for non-linear dimensionality reduction. Basic principle in both the technique is that local neighborhood relationship is maintained. The results have beencompared for both the techniques on artificially generated data set using MATLAB software.
References
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