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

被引：3

作者：

Fang L. ^{[1
]}

Lv Y. ^{[1
]}

Ma L. ^{[1
]}

Qi Z. ^{[1
]}

Zhao Y. ^{[1
]}

机构：

[1] Mechanical Engineering College, Heping West Road #97, Shijiazhuang

来源：

International Journal of Computer Applications in Technology | 2017年 / 56卷 / 03期

关键词：

Dimensionality reduction; LLTSA; Neighbourhood adaptive; Pattern recognition; Semi-supervised learning;

D O I：

10.1504/IJCAT.2017.088193

中图分类号：

学科分类号：

摘要：

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. Copyright © 2017 Inderscience Enterprises Ltd.

引用

页码：244 / 252

页数：8

共 23 条

[1]

Cao Q., Ying Y.M., Li P., Similarity metric learning for face recognition, Proceedings of The 2013 International Conference on Computer Vision, pp. 2408-2415, (2013)

[2]

Dorji T.C., Categorising texts more accurately with field association terms, International Journal of Computer Applications in Technology, 52, 2-3, pp. 150-159, (2015)

[3]

Ebtehaj A.M., Bras R.L., Foufoula-Georgiou E., Shrunken locally linear embedding for passive microwave retrieval of precipitation, Transactions on Geoscience and Remote Sensing, 7, 53, pp. 3720-3736, (2015)

[4]

Eleni M., Symeon P., Yiannis K., Learning to detect concepts with approximate Laplacian Eigenmaps in large-scale and online settings, International Journal of Multimedia Information Retrieval, 2, 4, pp. 95-111, (2015)

[5]

Gui J., Jia W., Zhu L., Wang S.-L., Huang D.S., Locality preserving discriminant projections for face and palmprint recognition, Neurocomputing, 73, pp. 2696-2707, (2010)

[6]

Harada T., Tsuda K., Suzuki N., Fujita Y., The estimate method of the omission of Japanese inquiry texts using an LDA algorithm, International Journal of Computer Applications in Technology, 52, 2-3, pp. 186-195, (2015)

[7]

He D.C., Zhang H.J., Hao W.N., Zhang R., A robust Parzen window mutual information estimator for feature selection with label noise, Intelligent Data Analysis, 6, 19, pp. 1199-1212, (2015)

[8]

Jing W.P., Liu Y.Q., Shao H.R., Reliability-aware DAG scheduling with primary-backup in cloud computing, International Journal of Computer Applications in Technology, 52, 1, pp. 86-93, (2015)

[9]

Li F., Chyub M.K., Wang J.X., Tang B., ‘Life grade recognition of rotating machinery based on supervised orthogonal linear local tangent space alignment and optimal supervised fuzzy c-means clustering, Measurement, 73, pp. 384-400, (2015)

[10]

Li F., Tang B.P., Yang R.S., Rotating machine fault diagnosis using dimension reduction with linear local tangent space alignment, Measurement, 8, 46, pp. 2525-2539, (2013)

← 1 2 3 →