@inproceedings{94113f3dfa7f45d8a3e83bb8a15289db,
title = "An incremental manifold learning algorithm based on the small world model",
abstract = "Manifold learning can perform nonlinear dimensionality reduction in the high dimensional space. ISOMAP, LLE, Laplacian Eigenmaps, LTSA and Multilayer autoencoders are representative algorithms. Most of them are only defined on the training sets and are executed as a batch mode. They don't provide a model or a formula to map the new data into the low dimensional space. In this paper, we proposed an incremental manifold learning algorithm based on the small world model, which generalizes ISOMAP to new samples. At first, k nearest neighbors and some faraway points are selected from the training set for each new sample. Then the low dimensional embedding of the new sample is obtained by preserving the geodesic distances between it and those points. Experiments demonstrate that new samples can effectively be projected into the low dimensional space with the presented method and the algorithm has lower complexity.",
keywords = "incremental algorithm, ISOMAP, manifold learning, small world model",
author = "Lukui Shi and Qingxin Yang and Enhai Liu and Jianwei Li and Yongfeng Dong",
year = "2010",
doi = "10.1007/978-3-642-15621-2\_36",
language = "英语",
isbn = "3642156207",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
number = "PART 1",
pages = "324--332",
booktitle = "Life System Modeling and Intelligent Computing - International Conference on LSMS 2010 and ICSEE 2010, Proceedings",
edition = "PART 1",
note = "2010 International Conference on Life System Modeling and Simulation, LSMS 2010 and the 2010 International Conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2010 ; Conference date: 17-09-2010 Through 20-09-2010",
}