نتایج جستجو برای: الگوریتم isomap
تعداد نتایج: 22715 فیلتر نتایج به سال:
The monkey anterior intraparietal area (AIP) encodes visual information about three-dimensional object shape that is used to shape the hand for grasping. We modeled shape tuning in visual AIP neurons and its relationship with curvature and gradient information from the caudal intraparietal area (CIP). The main goal was to gain insight into the kinds of shape parameterizations that can account f...
Linear dimensionality reduction techniques, notably principal component analysis, are widely used in climate data analysis as a means to aid in the interpretation of datasets of high dimensionality. These linear methods may not be appropriate for the analysis of data arising from nonlinear processes occurring in the climate system. Numerous techniques for nonlinear dimensionality reduction have...
روشهای کاهش بُعد غیرخطی، در دهها خیر بار دیگر مورد توجه محافل علمی قرار گرفتهاند.با تمرکز محققان علم کامپیوتر بر این مسئله، در چند سال اخیر مجموعهای از ابزارها به وجود آمدهاند که کاربردهای آنها در دادهکاوی، پردازش تصویر، طبقهبندی، تحلیل ونمایاندن دادگان رو به افزایش است.در این میان یادگیری منیفلد ابزاری قدرتمند برای کاهش بعد...
As an effective way in finding the underlying parameters of a high-dimension space, manifold learning is popular in nonlinear dimensionality reduction which makes high-dimensional data easily to be observed and analyzed. In this paper, Isomap, one of the most famous manifold learning algorithms, is applied to process closing prices of stocks of CSI 300 index from September 2009 to October 2011....
Learning a good distance function is crucial in a lot of applications. Different Learning distance with different will effect the performance of application in different ways. The most closely related application is classification and retrieval task. In this report, three different distance learning algorithm, LMNN, Isomap and SimpleNPKL, is compared via their performance in three different kin...
Algorithms for nonlinear dimensionality reduction (NLDR) find meaningful hidden low-dimensional structures in a high-dimensional space. Current algorithms for NLDR are Isomaps, Local Linear Embedding and Laplacian Eigenmaps. Isomaps are able to reliably recover lowdimensional nonlinear structures in high-dimensional data sets, but suffer from the problem of short-circuiting, which occurs when t...
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