Article: Improved linear local tangent space alignment and its application to pattern recognition Journal: International Journal of Computer Applications in Technology (IJCAT) 2017 Vol.56 No.3 pp.244 - 252 Abstract: Considering the drawbacks of the linear local tangent space alignment, a Semi-Supervised Neighbourhood Adaptive Linear Local Tangent Space Alignment (SSNA-LLTSA) is proposed. The distance metric combining the Cosine similarity and the Euclidean distance is used in the algorithm instead of the Euclidean distance, and the algorithm realises the semi-supervised learning and neighbourhood adaptive adjustment by integrating some of the known category information and the method of Parzen window density estimation into the dimensionality reduction process. The simulation experiment of UCI standard data sets and the pattern recognition example of hydraulic pump show that the redefined distance metric has better performance than the Euclidean distance, and SSNA-LLTSA can overcome the defect that LLTSA is unsupervised. Meanwhile, the capability of neighbourhood adaptive adjustment makes the algorithm find the low-dimensional manifold of the data sets more effectively, which can further improve the accuracy of pattern recognition. Inderscience Publishers - linking academia, business and industry through research

Title: Improved linear local tangent space alignment and its application to pattern recognition

Authors: Liqing Fang; Yan Lv; Leilei Ma; Ziyuan Qi; Yulong Zhao

Addresses: Mechanical Engineering College, Heping West Road #97, Shijiazhuang 050003, China ' Mechanical Engineering College, Heping West Road #97, Shijiazhuang 050003, China ' Mechanical Engineering College, Heping West Road #97, Shijiazhuang 050003, China ' Mechanical Engineering College, Heping West Road #97, Shijiazhuang 050003, China ' Mechanical Engineering College, Heping West Road #97, Shijiazhuang 050003, China

Abstract: Considering the drawbacks of the linear local tangent space alignment, a Semi-Supervised Neighbourhood Adaptive Linear Local Tangent Space Alignment (SSNA-LLTSA) is proposed. The distance metric combining the Cosine similarity and the Euclidean distance is used in the algorithm instead of the Euclidean distance, and the algorithm realises the semi-supervised learning and neighbourhood adaptive adjustment by integrating some of the known category information and the method of Parzen window density estimation into the dimensionality reduction process. The simulation experiment of UCI standard data sets and the pattern recognition example of hydraulic pump show that the redefined distance metric has better performance than the Euclidean distance, and SSNA-LLTSA can overcome the defect that LLTSA is unsupervised. Meanwhile, the capability of neighbourhood adaptive adjustment makes the algorithm find the low-dimensional manifold of the data sets more effectively, which can further improve the accuracy of pattern recognition.

Keywords: pattern recognition; dimensionality reduction; semi-supervised learning; neighbourhood adaptive; LLTSA.

DOI: 10.1504/IJCAT.2017.088193

International Journal of Computer Applications in Technology, 2017 Vol.56 No.3, pp.244 - 252

Received: 21 Jul 2016
Accepted: 23 Nov 2016
Published online: 29 Nov 2017 *

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Title: Improved linear local tangent space alignment and its application to pattern recognition

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