Kernel Local Linear Discriminate Method for Dimensionality Reduction and Its Application in Machinery Fault Diagnosis
Joint Authors
Shi, Kunju
Liu, Shulin
Zhang, Hongli
Wang, Bo
Source
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-02-27
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
Dimensionality reduction is a crucial task in machinery fault diagnosis.
Recently, as a popular dimensional reduction technology, manifold learning has been successfully used in many fields.
However, most of these technologies are not suitable for the task, because they are unsupervised in nature and fail to discover the discriminate structure in the data.
To overcome these weaknesses, kernel local linear discriminate (KLLD) algorithm is proposed.
KLLD algorithm is a novel algorithm which combines the advantage of neighborhood preserving projections (NPP), Floyd, maximum margin criterion (MMC), and kernel trick.
KLLD has four advantages.
First of all, KLLD is a supervised dimension reduction method that can overcome the out-of-sample problems.
Secondly, short-circuit problem can be avoided.
Thirdly, KLLD algorithm can use between-class scatter matrix and inner-class scatter matrix more efficiently.
Lastly, kernel trick is included in KLLD algorithm to find more precise solution.
The main feature of the proposed method is that it attempts to both preserve the intrinsic neighborhood geometry of the increased data and exact the discriminate information.
Experiments have been performed to evaluate the new method.
The results show that KLLD has more benefits than traditional methods.
American Psychological Association (APA)
Shi, Kunju& Liu, Shulin& Zhang, Hongli& Wang, Bo. 2014. Kernel Local Linear Discriminate Method for Dimensionality Reduction and Its Application in Machinery Fault Diagnosis. Shock and Vibration،Vol. 2014, no. 2014, pp.1-11.
https://search.emarefa.net/detail/BIM-1047883
Modern Language Association (MLA)
Shi, Kunju…[et al.]. Kernel Local Linear Discriminate Method for Dimensionality Reduction and Its Application in Machinery Fault Diagnosis. Shock and Vibration No. 2014 (2014), pp.1-11.
https://search.emarefa.net/detail/BIM-1047883
American Medical Association (AMA)
Shi, Kunju& Liu, Shulin& Zhang, Hongli& Wang, Bo. Kernel Local Linear Discriminate Method for Dimensionality Reduction and Its Application in Machinery Fault Diagnosis. Shock and Vibration. 2014. Vol. 2014, no. 2014, pp.1-11.
https://search.emarefa.net/detail/BIM-1047883
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1047883