Titlebook: Geometric Structure of High-Dimensional Data and Dimensionality Reduction; Jianzhong Wang Book 2012 Higher Education Press, Beijing and Sp

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Kevin McDermott,Vítězslav Sommerrs, which represent the objects of interest. In the second type, the data describe the similarities (or dissimilarities) of objects that cannot be digitized or hidden. The output of a DR processing with an input of the first type is a low-dimensional data set, having the same cardinality as the inpu

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Jozef Lacko,Ladislav Kusňír,Ivan Slameňetween the pairs of all neighbors of each point in the data set. Since the method keeps the local maximum variance in dimensionality reduction processing, it is called maximum variance unfolding (MVU). Like multidimensional scaling (MDS), MVU can be applied to the cases that only the local similarit

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https://doi.org/10.1007/978-3-322-82834-7n a low-dimentional manifold .. Let . be the coordinate mapping on . so that . = .(.)is a DR of .. Each component of the coordinate mapping . is a linear function on .. Hence, all components of . nearly reside on the numerically null space of the Laplace-Beltrsmi operator on .. In Leigs method, a La

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https://doi.org/10.1007/978-1-4612-0553-1 conceptual framework of HLLE may be viewed as a modification of the Laplacian Eigenmaps framework. Let . be the observed high-dimensional data which reside on a low-dimentional manifold . and . be the coordinate mapping on . so that . = .(.)is a DR of .. In Laplacian eigenmaps method, . is found in