Understanding nonlinear dynamics
Understanding nonlinear dynamics
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Self-Organizing Maps
Estimating the Intrinsic Dimension of Data with a Fractal-Based Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
Non-Linear Dimensionality Reduction
Advances in Neural Information Processing Systems 5, [NIPS Conference]
A Nonlinear Mapping for Data Structure Analysis
IEEE Transactions on Computers
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In this paper, we propose an adaptive incremental nonlinear dimensionality reduction algorithm for data stream in adaptive Self-organizing Isometric Embedding [1][3] framework. Assuming that each sampling point of underlying manifold and its adaptive neighbors [3] can preserve the principal directions of the regions that they reside on, our algorithm need only update the geodesic distances between anchors and all the other points, as well as distances between neighbors of incremental points and all the other points when a new point arrives. Under the above assumption, our algorithms can realize an approximate linear time complexity embedding of incremental points and effectively tradeoff embedding precision and time cost.