Adaptive Neighborhood Graph for LTSA Learning Algorithm without FreeParameter
نویسندگان
چکیده
منابع مشابه
Adaptive Neighborhood Graph for LTSA Learning Algorithm without Free-Parameter
Local Tangent Space Alignment (LTSA) algorithm is a classic local nonlinear manifold learning algorithm based on the information about local neighborhood space, i.e., local tangent space with respect to each point in dataset, which aims at finding the low-dimension intrinsic structure lie in high dimensional data space for the purpose of dimensionality reduction. In this paper, we present a nov...
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Locally Linear Embedding (LLE) algorithm is the first classic nonlinear manifold learning algorithm based on the local structure information about the data set, which aims at finding the low-dimension intrinsic structure lie in high dimensional data space for the purpose of dimensionality reduction. One deficiency appeared in this algorithm is that it requires users to give a free parameter k w...
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Let G be a simple graph with vertex set {v1, v2, … , vn}. The common neighborhood graph of G, denoted by con(G), is a graph with vertex set {v1, v2, … , vn}, in which two vertices are adjacent if and only if they have at least one common neighbor in the graph G. In this paper, we compute the common neighborhood of some composite graphs. In continue, we investigate the relation between hamiltoni...
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ژورنال
عنوان ژورنال: International Journal of Computer Applications
سال: 2011
ISSN: 0975-8887
DOI: 10.5120/2348-3070