Improved Algorithm Base on Locally Linear Embedding

2014 ◽  
Vol 644-650 ◽  
pp. 2160-2163 ◽  
Author(s):  
Shi Min Liu ◽  
Yan Ni Deng ◽  
Yuan Xing Lv
Keyword(s):  
Learning Algorithm ◽  
Recognition Rate ◽  
Feature Space ◽  
Locally Linear Embedding ◽  
Dispersion Matrix ◽  
Redundant Information ◽  
Training Samples ◽  
Linear Embedding ◽  
Low Dimensional ◽  
Locally Linear

Locally linear embedding algorithm (LLE) , It makes up the shortcomings that the manifold learning algorithm can be only applied to training samples but not be extended to test samples . However, due to the presence of its Low-dimensional feature space redundant information,and its sample category information does not integrate into a low-dimensional embedding. For this shortcoming, here we introduce the two improved algorithms:the local linear maximum dispersion matrix algorithm (FSLLE) and the adaptive algorithm (ALLE), and the combinations of the above two algorithms.With this experience,combined Garbol and locally linear embedding algorithm (LLE) to compare each conclusion. The results proved to be effective elimination of redundant information among basis vectors and improve the recognition rate.

Machinery Fault Diagnosis Based on Supervised Locally Linear Embedding

2014 ◽  
Vol 536-537 ◽  
pp. 49-52
Author(s):  
Xiang Wang ◽  
Yuan Zheng
Keyword(s):  
Fault Diagnosis ◽  
Learning Algorithm ◽  
Classification Performance ◽  
Fault Classification ◽  
Locally Linear Embedding ◽  
Diagnosis Method ◽  
Linear Embedding ◽  
Low Dimensional ◽  
Machinery Fault Diagnosis ◽  
Locally Linear

Fault diagnosis is essentially a kind of pattern recognition. In this paper propose a novel machinery fault diagnosis method based on supervised locally linear embedding is proposed first. The approach first performs the recently proposed manifold learning algorithm locally linear embedding on the high-dimensional fault signal samples to learn the intrinsic embedded multiple manifold features corresponding to different fault modes. Supervised locally linear embedding not only can map them into a low-dimensional embedded space to achieve fault feature extraction, but also can deal with new fault samples. Finally fault classification is carried out in the embedded manifold space. The ball bearing fault signals are used to validate the proposed fault diagnosis method. The results indicate that the proposed approach obviously improves the fault classification performance and outperforms the other traditional approaches.

Supervised Locally Linear Embedding for Fault Diagnosis

2010 ◽  
Vol 139-141 ◽  
pp. 2599-2602
Author(s):  
Zheng Wei Li ◽  
Ru Nie ◽  
Yao Fei Han
Keyword(s):  
Fault Diagnosis ◽  
Recognition Performance ◽  
Feature Space ◽  
Difficult Problem ◽  
Fault Classification ◽  
High Dimensional ◽  
Locally Linear Embedding ◽  
Local Linear Embedding ◽  
Linear Embedding ◽  
Locally Linear

Fault diagnosis is a kind of pattern recognition problem and how to extract diagnosis features and improve recognition performance is a difficult problem. Local Linear Embedding (LLE) is an unsupervised non-linear technique that extracts useful features from the high-dimensional data sets with preserved local topology. But the original LLE method is not taking the known class label information of input data into account. A new characteristics similarity-based supervised locally linear embedding (CSSLLE) method for fault diagnosis is proposed in this paper. The CSSLLE method attempts to extract the intrinsic manifold features from high-dimensional fault data by computing Euclidean distance based on characteristics similarity and translate complex mode space into a low-dimensional feature space in which fault classification and diagnosis are carried out easily. The experiments on benchmark data and real fault dataset demonstrate that the proposed approach obtains better performance compared to SLLE, and it is an accurate technique for fault diagnosis.

Improved LLE Algorithm Based on Supervision

2013 ◽  
Vol 427-429 ◽  
pp. 1900-1902
Author(s):  
Li Li Gan ◽  
Si Si Chen ◽  
Juan Juan Cao ◽  
Zhong Yong Wu
Keyword(s):  
Manifold Learning ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Locally Linear Embedding ◽  
Classification Problems ◽  
Series Of Experiments ◽  
Linear Embedding ◽  
Classification Information ◽  
Locally Linear

It focuses on locally linear embedding algorithm into LLE proposed supervised locally linear embedding algorithm (SLLE). That supervised manifold learning algorithm, which introduced adjustable parameters to effectively use the classification information, so as to make the SLLE have a stronger effect for classification problems. Finally, through a series of experiments to fully illustrate the proposed improvement of the effectiveness of the algorithm, the proposed oversight of the manifold learning algorithm can more effectively enhance manifold learning algorithms for classification problems proficiency.

Vibration Fault Diagnosis for Wind Turbine Based on Enhanced Supervised Locally Linear Embedding

2014 ◽  
Vol 1008-1009 ◽  
pp. 983-987
Author(s):  
Xiang Wang ◽  
Yuan Zheng
Keyword(s):  
Fault Diagnosis ◽  
Wind Turbine ◽  
Learning Algorithm ◽  
Operating Conditions ◽  
Fault Classification ◽  
Maintenance Cost ◽  
Locally Linear Embedding ◽  
Diagnosis Method ◽  
Linear Embedding ◽  
Locally Linear

Fault diagnosis for wind turbine is an important task for reducing their maintenance cost. However, the non-stationary dynamic operating conditions of wind turbines pose a challenge to fault diagnosis for wind turbine. Fault diagnosis is essentially a kind of pattern recognition. In this paper, a novel fault diagnosis method based on enhanced supervised locally linear embedding is proposed for wind turbine. The approach first performs the recently proposed manifold learning algorithm locally linear embedding on the high-dimensional fault signal samples to learn the intrinsic embedded multiple manifold features corresponding to different fault modes. Enhanced supervised locally linear embedding not only can map them into a low-dimensional embedded space to achieve fault feature extraction, but also can deal with new fault samples. Finally fault classification is carried out in the embedded manifold space. The wind turbine gearbox ball bearing vibration fault signals are used to validate the proposed fault diagnosis method. The results indicate that the proposed approach obviously improves the fault classification performance and outperforms the other traditional approaches.

LOCALLY LINEAR EMBEDDING: A REVIEW

2011 ◽  
Vol 25 (07) ◽  
pp. 985-1008 ◽  
Author(s):  
JING CHEN ◽  
ZHENGMING MA
Keyword(s):  
High Dimensional Data ◽  
Dimensional Structure ◽  
High Dimensional ◽  
Locally Linear Embedding ◽  
Future Research ◽  
Nonlinear Dimensionality Reduction ◽  
The Novel ◽  
Linear Embedding ◽  
Low Dimensional ◽  
Locally Linear

The goal of nonlinear dimensionality reduction is to find the meaningful low dimensional structure of the nonlinear manifold from the high dimensional data. As a classic method of nonlinear dimensional reduction, locally linear embedding (LLE) is more and more attractive to researchers due to its ability to deal with large amounts of high dimensional data and its noniterative way of finding the embeddings. However, several problems in the LLE algorithm still remain open, such as its sensitivity to noise, inevitable ill-conditioned eigenproblems, the inability to deal with the novel data, etc. The existing extensions are comprehensively reviewed and discussed classifying into different categories in this paper. Their strategies, advantages/disadvantages and performances are elaborated. By generalizing different tactics in various extensions related to different stages of LLE and evaluating their performances, several promising directions for future research have been suggested.

Locally Linear Embedding Based on Relative Manifold

2009 ◽  
Vol 20 (9) ◽  
pp. 2376-2386 ◽  
Author(s):  
Gui-Hua WEN ◽  
Ting-Hui LU ◽  
Li-Jun JIANG ◽  
Jun WEN
Keyword(s):  
Locally Linear Embedding ◽  
Linear Embedding ◽  
Locally Linear
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