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ICONIP
2004

Non-linear Dimensionality Reduction by Locally Linear Isomaps

9 years 15 days ago
Non-linear Dimensionality Reduction by Locally Linear Isomaps
Algorithms for nonlinear dimensionality reduction (NLDR) find meaningful hidden low-dimensional structures in a high-dimensional space. Current algorithms for NLDR are Isomaps, Local Linear Embedding and Laplacian Eigenmaps. Isomaps are able to reliably recover lowdimensional nonlinear structures in high-dimensional data sets, but suffer from the problem of short-circuiting, which occurs when the neighborhood distance is larger than the distance between the folds in the manifolds. We propose a new variant of Isomap algorithm based on local linear properties of manifolds to increase its robustness to short-circuiting. We demonstrate that the proposed algorithm works better than Isomap algorithm for normal, noisy and sparse data sets.
Ashutosh Saxena, Abhinav Gupta, Amitabha Mukerjee
Added 31 Oct 2010
Updated 31 Oct 2010
Type Conference
Year 2004
Where ICONIP
Authors Ashutosh Saxena, Abhinav Gupta, Amitabha Mukerjee
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