Supervised Subspace Learning with Multi-class Lagrangian SVM on the Grassmann Manifold

2011 ◽  
pp. 241-250 ◽  
Author(s):  
Duc-Son Pham ◽  
Svetha Venkatesh
Keyword(s):  
Grassmann Manifold ◽  
Subspace Learning

scholarly journals A Riemannian gossip approach to subspace learning on Grassmann manifold

2019 ◽  
Vol 108 (10) ◽  
pp. 1783-1803 ◽  
Author(s):  
Bamdev Mishra ◽  
Hiroyuki Kasai ◽  
Pratik Jawanpuria ◽  
Atul Saroop
Keyword(s):  
Grassmann Manifold ◽  
Subspace Learning

Grassmannian Spectral Regression for Learning and Classification

2015 ◽  
Vol 24 (04) ◽  
pp. 1540015
Author(s):  
Sherif Azary ◽  
Andreas Savakis
Keyword(s):  
Grassmann Manifold ◽  
Learning Algorithm ◽  
Subspace Learning ◽  
Classification Systems ◽  
High Dimensional ◽  
Action Classification ◽  
Computational Performance ◽  
Computationally Intensive ◽  
Low Dimensional ◽  
Spectral Regression

Computational performance associated with high dimensional data is a common challenge for real-world action classification systems. Subspace learning, and manifold learning in particular, have received considerable attention as means of finding efficient low-dimensional representations that lead to better classification and efficient processing. A Grassmann manifold is a space that promotes smooth surfaces where points represent subspaces. In this paper, Grassmannian Spectral Regression (GRASP) is presented as a Grassmann inspired subspace learning algorithm that combines the benefits of Grassmann manifolds and spectral regression for fast and accurate classification. GRASP involves embedding high dimensional action subspaces as individual points onto a Grassmann manifold, kernelizing the embeddings onto a projection space, and then applying Spectral Regression for fast and accurate action classification. Furthermore, spatiotemporal action descriptions called Motion History Surfaces and Motion Depth Surfaces are utilized. The effectiveness of GRASP is illustrated for computationally intensive, multi-view and 3D action classification datasets.

Automatically Outlier-Resisting Subspace Learning

2011 ◽  
Vol 30 (1) ◽  
pp. 176-179
Author(s):  
Yan-wei Pang ◽  
Zheng-kai Liu
Keyword(s):  
Subspace Learning

Multiview Shared Subspace Learning Across Speakers and Speech Commands

2019 ◽  
Author(s):  
Krishna Somandepalli ◽  
Naveen Kumar ◽  
Arindam Jati ◽  
Panayiotis Georgiou ◽  
Shrikanth Narayanan
Keyword(s):  
Subspace Learning

Learning task-driving affinity matrix for accurate multi-view clustering through tensor subspace learning

2021 ◽  
Vol 563 ◽  
pp. 290-308
Author(s):  
Haiyan Wang ◽  
Guoqiang Han ◽  
Junyu Li ◽  
Bin Zhang ◽  
Jiazhou Chen ◽  
...  
Keyword(s):  
Learning Task ◽  
Subspace Learning ◽  
Affinity Matrix ◽  
Tensor Subspace
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