Incremental Line Tangent Space Alignment Algorithm

2007 ◽  
Author(s):  
Osama Abdel-Mannan ◽  
A. Ben Hamza ◽  
Amr Youssef
Keyword(s):  
Tangent Space ◽  
Alignment Algorithm ◽  
Line Tangent

Adaptive Discriminant Linear Local Tangent Space Alignment Algorithm on Face Recognition

2014 ◽  
Vol 989-994 ◽  
pp. 2381-2384
Author(s):  
Yuan Xing Lv ◽  
Yan Ni Deng ◽  
Yuan Shi ◽  
Qiang Li ◽  
Wen Peng
Keyword(s):  
Face Recognition ◽  
Tangent Space ◽  
Recognition Rate ◽  
Degree Of Polymerization ◽  
Local Geometry ◽  
Alignment Algorithm ◽  
Scatter Matrix ◽  
The Face ◽  
Local Tangent

This paper proposes an adaptive discriminant linear local tangent space alignment algorithm DALLTSA. On the basis of LLTSA algorithm adding adaptive and discriminant gets DALLTSA.DALLTSA not only combines characteristics in DLLTSA that maintain the local geometry and meets the maximum between-class scatter matrix, but also dynamically selects K-neighbor better to reflect the degree of polymerization between samples. Finally, the face recognition experiments based on Gabor [1] filter and DALLTSA shows that this algorithm improves the recognition rate and robustness.

Fusion Algorithm Based on Improved Linear Local Tangent Space Alignment Algorithm

2014 ◽  
Vol 602-605 ◽  
pp. 3447-3450
Author(s):  
Wen Peng ◽  
Yan Ni Deng ◽  
Yuan Shi ◽  
Yuan Xing Lv ◽  
Qiang Li
Keyword(s):  
Tangent Space ◽  
Learning Algorithm ◽  
Recognition Rate ◽  
Alignment Algorithm ◽  
Redundant Information ◽  
Fusion Algorithm ◽  
Scatter Matrix ◽  
Neighborhood Selection ◽  
Adaptive Neighborhood ◽  
Local Tangent

A uncorrelated adaptive discriminant linear local tangent space alignment (UDALLTSA) is proposed based on improved linear local tangent space alignment algorithm. The algorithm uses an adaptive neighborhood selection to select the appropriate neighborhood, and introduces curvature to amend the model, modifies the constraints of the objective function by use inter-class scatter matrix, and constraints on basis vectors to compute the best projection matrix. By comparing the results of the experiments, it shows that after integrating the discriminant information into the algorithm , uncorrelated constraints and adaptive neighborhood selection can well improve the recognition rate and robustness, thus, possessing good noise immunity, and eliminating redundant information of base vectors, make this fusion algorithm a supervised learning algorithm.

Fault Diagnosis Method Based on Supervised Manifold Learning and SVM

2011 ◽  
Vol 216 ◽  
pp. 223-227 ◽  
Author(s):  
Guang Bin Wang ◽  
Xue Jun Li ◽  
Zhi Cheng He ◽  
Y.Q. Kong
Keyword(s):  
Fault Diagnosis ◽  
Tangent Space ◽  
Roller Bearing ◽  
Support Vector ◽  
Alignment Algorithm ◽  
Bearing Fault Diagnosis ◽  
Svm Algorithm ◽  
Diagnosis Method ◽  
Fault Feature Extraction ◽  
Local Tangent

In order to better identify the fault of bearing,one new fualt diagnosis method based on supervised Linear local tangent space alignment (SLLTSA) and support vector machine (SVM) is proposed..In this methd, the supervised learning is embedded into the linear local tangent space alignment algorithm,making full use of experience category information for fault feature extraction, and then using linear transformation matrix to fast process the new monitoring data, finally distinguishing fault status of the incremental data by nonlinear SVM algorithm. The experiment result for roller bearing fault diagnosis shows that SLLTSA-SVM method has better diagnosis effect than related unsupervised methods.

Face Recognition Based on Gabor Filter and Improved Linear Local Tangent Space Alignment Algorithm

2014 ◽  
Vol 989-994 ◽  
pp. 4091-4094
Author(s):  
Xue Yan Xu ◽  
Jiao Yu Liu ◽  
Yuan Shi ◽  
Tuo Deng
Keyword(s):  
Face Recognition ◽  
Tangent Space ◽  
Gabor Filter ◽  
Recognition Rate ◽  
Gabor Wavelet ◽  
Alignment Algorithm ◽  
Gabor Filtering ◽  
The Face ◽  
Secondary Feature ◽  
Local Tangent

In this paper, Gabor filtering and linear local tangent space alignment algorithm and its improved algorithm are used on face recognition. The Gabor wavelet transform can detect the image information in different directions and scales, according to its selective direction and frequency characteristics. The LLTSA reduces the dimension of the sample while the LLTSA and the other improved algorithms extract secondary feature. Experiment and analyze the average recognition rate of the LLTSA and its improved algorithms with the variation of dimension. The experiment results show the effectiveness of the method, increasing the face recognition accuracy.

Fault Diagnosis Method Based on Supervised Incremental Local Tangent Space Alignment and SVM

2010 ◽  
Vol 34-35 ◽  
pp. 1233-1237 ◽  
Author(s):  
Guang Bin Wang ◽  
Xian Qiong Zhao ◽  
Yu Hui He
Keyword(s):  
Fault Diagnosis ◽  
Tangent Space ◽  
Roller Bearing ◽  
Support Vector ◽  
Alignment Algorithm ◽  
Bearing Fault Diagnosis ◽  
Vector Machines ◽  
Diagnosis Method ◽  
Fault Feature Extraction ◽  
Local Tangent

To enhance the effect of fault diagnosis, a new fualt diagnosis method based on supervised incremental local tangent space alignment (SILTSA) and support vector machine (SVM) is proposed. The supervised learning approach is embedded into the incremental local tangent space alignment algorithm, to realize fault feature extraction and new data processing for equipment fault signal, and then correctly classify the faults by non-linear support vector machines. The experiment result for roller bearing fault diagnosis shows that SILTSA-SVM method has better diagnosis effect to related methods

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