Abstract
LLE(Local linear embedding) and Isomap are widely used approaches for dimension reduction. For LLE, the neighborhood selection approach is an important research issue. For different types of datasets, we need different neighborhood selection approaches to have better chance for finding reasonable representation within the required number of dimensions. In this paper, the ε-distance approach and a modified version of k-nn method are introduced. For LLE and Isomap, the eigenvectors obtained from these methods are much more discussed, but there are more information hidden in the corresponding eigenvalues which can be used for finding embeddings contains more data information.
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Liou, JW., Liou, CY. (2012). Neighborhood Selection and Eigenvalues for Embedding Data Complex in Low Dimension. In: Pan, JS., Chen, SM., Nguyen, N.T. (eds) Intelligent Information and Database Systems. ACIIDS 2012. Lecture Notes in Computer Science(), vol 7196. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28487-8_43
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DOI: https://doi.org/10.1007/978-3-642-28487-8_43
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