Algorithms for clustering data
Algorithms for clustering data
GTM: the generative topographic mapping
Neural Computation
Semi-Supervised Learning on Riemannian Manifolds
Machine Learning
A Semi-supervised SVM for Manifold Learning
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
Manifold constrained finite gaussian mixtures
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
MCPR'11 Proceedings of the Third Mexican conference on Pattern recognition
CIARP'11 Proceedings of the 16th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Cartogram visualization for nonlinear manifold learning models
Data Mining and Knowledge Discovery
A multi-manifold semi-supervised Gaussian mixture model for pattern classification
Pattern Recognition Letters
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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.