Mixtures of probabilistic principal component analyzers
Neural Computation
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Generalized Principal Component Analysis (GPCA)
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised learning of image manifolds by semidefinite programming
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Clustering appearances of objects under varying illumination conditions
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Translated Poisson Mixture Model for Stratification Learning
International Journal of Computer Vision
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Central and subspace clustering methods are at the core of many segmentation problems in computer vision. However, both methods fail to give the correct segmentation in many practical scenarios, e.g., when data points are close to the intersection of two subspaces or when two cluster centers in different subspaces are spatially close. In this paper, we address these challenges by considering the problem of clustering a set of points lying in a union of subspaces and distributed around multiple cluster centers inside each subspace. We propose a generalization of Kmeans and Ksubspaces that clusters the data by minimizing a cost function that combines both central and subspace distances. Experiments on synthetic data compare our algorithm favorably against four other clustering methods. We also test our algorithm on computer vision problems such as face clustering with varying illumination and video shot segmentation of dynamic scenes.