Nonlinear Manifold Learning for Data Stream
نویسندگان
چکیده
There has been a renewed interest in understanding thestructure of high dimensional data set based on manifoldlearning. Examples include ISOMAP [25], LLE [20]and Laplacian Eigenmap [2] algorithms. Most of thesealgorithms operate in a “batch” mode and cannot beapplied efficiently for a data stream. We propose anincremental version of ISOMAP. Our experiments notonly demonstrate the accuracy and efficiency of theproposed algorithm, but also reveal interesting behaviorof the ISOMAP as the size of available data increases.
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بهبود مدل تفکیککننده منیفلدهای غیرخطی بهمنظور بازشناسی چهره با یک تصویر از هر فرد
Manifold learning is a dimension reduction method for extracting nonlinear structures of high-dimensional data. Many methods have been introduced for this purpose. Most of these methods usually extract a global manifold for data. However, in many real-world problems, there is not only one global manifold, but also additional information about the objects is shared by a large number of manifolds...
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