Patch-Based Hippocampus Segmentation Using a Local Subspace Learning Method

2016 ◽  
pp. 86-94
Author(s):  
Yan Wang ◽  
Xi Wu ◽  
Guangkai Ma ◽  
Zongqing Ma ◽  
Ying Fu ◽  
...  
Keyword(s):  
Subspace Learning ◽  
Learning Method ◽  
Hippocampus Segmentation

Weighted multi-view common subspace learning method

2021 ◽  
Author(s):  
Jing An ◽  
Xiaoxia Liu ◽  
Mei Shi ◽  
Jun Guo ◽  
Xiaoqing Gong ◽  
...  
Keyword(s):  
Subspace Learning ◽  
Learning Method

DISCRIMINATIVE LOCAL LEARNING PROJECTION FOR FACE RECOGNITION

2011 ◽  
Vol 25 (01) ◽  
pp. 83-97 ◽  
Author(s):  
YU CHEN ◽  
JIAN HUANG ◽  
XIAOHONG XU ◽  
JIANHUANG LAI
Keyword(s):  
Face Recognition ◽  
Local Structure ◽  
Estimation Error ◽  
Subspace Learning ◽  
High Dimensional ◽  
Local Geometry ◽  
Local Learning ◽  
Learning Method ◽  
Local Structures ◽  
Face Images

Subspace learning method has commonly been used as a popular way to understand high dimensional data such as face images. In this paper, a novel subspace learning method called Discriminative Local Learning Projection (DLLP) is proposed for face recognition. By characterizing the local structures and dissimilarities between the supervised data manifolds, a linear transformation that can maximize the dissimilarities between all manifolds and simultaneously minimize the local estimation error can be computed. Thus the proposed algorithm embeds the discriminative information as well as the local geometry of samples into the objective function. And the abilities of preserving the local structure in each manifold and classification are both combined into the algorithm. Extensive experiments on face databases demonstrate the effectiveness of DLLP.

Discriminative Low-Rank Subspace Learning with Nonconvex Penalty

2019 ◽  
Vol 33 (10) ◽  
pp. 1951006
Author(s):  
Kan Xie ◽  
Wei Liu ◽  
Yue Lai ◽  
Weijun Li
Keyword(s):  
Face Recognition ◽  
State Of The Art ◽  
Subspace Learning ◽  
Experimental Results ◽  
Low Rank ◽  
Learning Method ◽  
Original Solution ◽  
Discriminative Subspace ◽  
Low Rank Representation ◽  
Facial Images

Subspace learning has been widely utilized to extract discriminative features for classification task, such as face recognition, even when facial images are occluded or corrupted. However, the performance of most existing methods would be degraded significantly in the scenario of that data being contaminated with severe noise, especially when the magnitude of the gross corruption can be arbitrarily large. To this end, in this paper, a novel discriminative subspace learning method is proposed based on the well-known low-rank representation (LRR). Specifically, a discriminant low-rank representation and the projecting subspace are learned simultaneously, in a supervised way. To avoid the deviation from the original solution by using some relaxation, we adopt the Schatten [Formula: see text]-norm and [Formula: see text]-norm, instead of the nuclear norm and [Formula: see text]-norm, respectively. Experimental results on two famous databases, i.e. PIE and ORL, demonstrate that the proposed method achieves better classification scores than the state-of-the-art approaches.

A sequential subspace learning method and its application to dynamic texture analysis

2007 ◽  
Vol 185 (2) ◽  
pp. 834-843
Author(s):  
Zejian Yuan ◽  
Yanyun Qu ◽  
Chao Yang ◽  
Yuehu Liu
Keyword(s):  
Texture Analysis ◽  
Subspace Learning ◽  
Learning Method ◽  
Dynamic Texture
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