Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Computational Framework for Segmentation and Grouping
Computational Framework for Segmentation and Grouping
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
An Algorithm for Finding Intrinsic Dimensionality of Data
IEEE Transactions on Computers
Enhanced supervised locally linear embedding
Pattern Recognition Letters
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 2
Identifying multiple spatiotemporal patterns: A refined view on terrestrial photosynthetic activity
Pattern Recognition Letters
An effective double-bounded tree-connected Isomap algorithm for microarray data classification
Pattern Recognition Letters
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Isomap is a highly popular manifold learning and dimensionality reduction technique that effectively performs multidimensional scaling on estimates of geodesic distances. However, the resulting output is extremely sensitive to parameters that control the selection of neighbors at each point. To date, no principled way of setting these parameters has been proposed, and in practice they are often tuned ad hoc, sometimes empirically based on prior knowledge of the desired output. In this paper we propose a parameterless technique that adaptively defines the neighborhood at each input point based on intrinsic dimensionality and local tangent orientation. In addition to eliminating the guesswork associated with parameter configuration, the adaptive nature of this technique enables it to select optimal neighborhoods locally at each point, resulting in superior performance.