Nonlinear Dimensionality Reduction via Path-Based Isometric Mapping.
IEEE Transactions on Pattern Analysis and Machine Intelligence(2016)
Abstract
Nonlinear dimensionality reduction methods have demonstrated top-notch performance in many pattern recognition and image classification tasks. Despite their popularity, they suffer from highly expensive time and memory requirements, which render them inapplicable to large-scale datasets. To leverage such cases we propose a new method called “Path-Based Isomap”. Similar to Isomap, we exploit geodes...
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Key words
Manifolds,Optimization,Complexity theory,Principal component analysis,Approximation methods,Approximation algorithms,Estimation
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