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The hybrid of semisupervised manifold learning and spectrum kernel for classification
Author(s) -
Shen Liang,
Xu Qingsong,
Cao Dongsheng,
Liang Yizeng,
Dai Hongshuai
Publication year - 2018
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/cem.2955
Subject(s) - artificial intelligence , kernel (algebra) , machine learning , classifier (uml) , computer science , pattern recognition (psychology) , multiple kernel learning , nonlinear dimensionality reduction , kernel method , manifold (fluid mechanics) , mathematics , support vector machine , dimensionality reduction , mechanical engineering , combinatorics , engineering
Manifold learning classification, as an advanced semisupervised learning algorithm in recent years, has gained great popularity in a variety of fields. Moreover, kernel methods are a group of algorithms for pattern analysis, the task of which is to find and study general types of relations in datasets. Thus, under the framework of kernel methods, manifold learning classifier has been introduced and explored to directly detect the intrinsic similarity by local and global information hidden in datasets. Two validation approaches were used to evaluate the performance of our models. Experiments indicate that the proposed model can be considered as an effective and alternative modeling algorithm, and it could be further applied to the areas of biochemical science, environmental analysis, clinical, etc.