1. Algebraic Clustering of Affine Subspaces
- Author
-
René Vidal and Manolis C. Tsakiris
- Subjects
FOS: Computer and information sciences ,Computer Vision and Pattern Recognition (cs.CV) ,Computer Science - Computer Vision and Pattern Recognition ,02 engineering and technology ,Affine geometry ,Affine combination ,Artificial Intelligence ,Affine hull ,0202 electrical engineering, electronic engineering, information engineering ,Mathematics ,Discrete mathematics ,business.industry ,Applied Mathematics ,020206 networking & telecommunications ,Affine plane ,Affine shape adaptation ,Algebra ,Affine coordinate system ,ComputingMethodologies_PATTERNRECOGNITION ,Computational Theory and Mathematics ,Affine space ,020201 artificial intelligence & image processing ,Computer Vision and Pattern Recognition ,Affine transformation ,Artificial intelligence ,business ,Software - Abstract
Subspace clustering is an important problem in machine learning with many applications in computer vision and pattern recognition. Prior work has studied this problem using algebraic, iterative, statistical, low-rank and sparse representation techniques. While these methods have been applied to both linear and affine subspaces, theoretical results have only been established in the case of linear subspaces. For example, algebraic subspace clustering (ASC) is guaranteed to provide the correct clustering when the data points are in general position and the union of subspaces is transversal . In this paper we study in a rigorous fashion the properties of ASC in the case of affine subspaces. Using notions from algebraic geometry, we prove that the homogenization trick , which embeds points in a union of affine subspaces into points in a union of linear subspaces, preserves the general position of the points and the transversality of the union of subspaces in the embedded space, thus establishing the correctness of ASC for affine subspaces.
- Published
- 2018