Title :
Semi-Supervised Discriminative Mutual Subspace Method
Author :
Zeng, Xianhua ; Zhong, Jingjing
Author_Institution :
Inst. of Cognitive Comput., Chongqing Univ. of Posts & Telecommun., Chongqing, China
Abstract :
Subspace recognition has recently attracted more attention for vector set or image set matching in machine learning and computer vision. In this paper, we firstly give a more simple proof of Procrustes metric (Theorem 2) than literature [1,7]. Then, a novel Semi-Supervised Discriminative Mutual Subspace Method (SS-DMSM) is proposed based on Procrustes metric. For finding a better discriminative subspace, our SS-DMSM algorithm sufficiently considers the intrinsic geometric information on Grassmann manifold that is the set of all subspaces, and effectively uses the label information of those training subspaces. Experimental results on Cambridge gesture database and ETH-80 database show that our SS-DMSM algorithm outperforms the classical MSM and CMSM algorithms.
Keywords :
computational geometry; computer vision; gesture recognition; image matching; learning (artificial intelligence); CMSM algorithms; Cambridge gesture database; ETH-80 database; Grassmann manifold; Procrustes metric; computer vision; image set matching; intrinsic geometric information; machine learning; semisupervised discriminative mutual subspace method; subspace recognition; vector set; Databases; Grassmann manifold; Principal angle; mutual subspace method; semi-supervised; subspace recognition;
Conference_Titel :
Cognitive Informatics & Cognitive Computing (ICCI*CC ), 2011 10th IEEE International Conference on
Conference_Location :
Banff, AB
Print_ISBN :
978-1-4577-1695-9
DOI :
10.1109/COGINF.2011.6016136