کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
536110 | 870459 | 2010 | 8 صفحه PDF | دانلود رایگان |
![عکس صفحه اول مقاله: Semi-supervised geodesic Generative Topographic Mapping Semi-supervised geodesic Generative Topographic Mapping](/preview/png/536110.png)
We present a novel semi-supervised model, SS-Geo-GTM, which stems from a geodesic distance-based extension of Generative Topographic Mapping that prioritizes neighbourhood relationships along a generated manifold embedded in the observed data space. With this, it improves the trustworthiness and the continuity of the low-dimensional representations it provides, while behaving robustly in the presence of noise. In SS-Geo-GTM, the model prototypes are linked by the nearest neighbour to the data manifold constructed by Geo-GTM. The resulting proximity graph is used as the basis for a class label propagation algorithm. The performance of SS-Geo-GTM is experimentally assessed, comparing positively with that of an Euclidean distance-based counterpart and with those of alternative manifold learning methods.
Journal: Pattern Recognition Letters - Volume 31, Issue 3, 1 February 2010, Pages 202–209