Multiple Bayesian discriminant functions for high-dimensional massive data classification

2016 ◽  
Vol 31 (2) ◽  
pp. 465-501 ◽  
Author(s):  
Jianfei Zhang ◽  
Shengrui Wang ◽  
Lifei Chen ◽  
Patrick Gallinari
Keyword(s):  
Data Classification ◽  
High Dimensional ◽  
Massive Data ◽  
Discriminant Functions

A modular eigen subspace scheme for high-dimensional data classification

2004 ◽  
Vol 20 (7) ◽  
pp. 1131-1143 ◽  
Author(s):  
Yang-Lang Chang ◽  
Chin-Chuan Han ◽  
Fan-Di Jou ◽  
Kuo-Chin Fan ◽  
K.S. Chen ◽  
...  
Keyword(s):  
High Dimensional Data ◽  
Data Classification ◽  
High Dimensional

A MULTI-CLASS SUPPORT VECTOR MACHINE: THEORY AND MODEL

2013 ◽  
Vol 12 (06) ◽  
pp. 1175-1199 ◽  
Author(s):  
MINGHE SUN
Keyword(s):  
Support Vector Machine ◽  
Quadratic Programming ◽  
Programming Model ◽  
Inner Product ◽  
High Dimensional ◽  
Support Vector ◽  
Test Problems ◽  
Discriminant Functions ◽  
Input Space ◽  
Feature Spaces

A multi-class support vector machine (M-SVM) is developed, its dual is derived, its dual is mapped to high dimensional feature spaces using inner product kernels, and its performance is tested. The M-SVM is formulated as a quadratic programming model. Its dual, also a quadratic programming model, is very elegant and is easier to solve than the primal. The discriminant functions can be directly constructed from the dual solution. By using inner product kernels, the M-SVM can be built and nonlinear discriminant functions can be constructed in high dimensional feature spaces without carrying out the mappings from the input space to the feature spaces. The size of the dual, measured by the number of variables and constraints, is independent of the dimension of the input space and stays the same whether the M-SVM is built in the input space or in a feature space. Compared to other models published in the literature, this M-SVM is equally or more effective. An example is presented to demonstrate the dual formulation and solution in feature spaces. Very good results were obtained on benchmark test problems from the literature.

An efficient random forests algorithm for high dimensional data classification

2018 ◽  
Vol 12 (4) ◽  
pp. 953-972 ◽  
Author(s):  
Qiang Wang ◽  
Thanh-Tung Nguyen ◽  
Joshua Z. Huang ◽  
Thuy Thi Nguyen
Keyword(s):  
Random Forests ◽  
High Dimensional Data ◽  
Data Classification ◽  
High Dimensional

scholarly journals A depth-based nearest neighbor algorithm for high-dimensional data classification

2019 ◽  
Vol 27 (6) ◽  
pp. 4082-4101
Author(s):  
Sandhya HARIKUMAR ◽  
Akhil ARAVINDAKSHAN SAVITHRI ◽  
Ramachandra KAIMAL
Keyword(s):  
Nearest Neighbor ◽  
High Dimensional Data ◽  
Data Classification ◽  
High Dimensional ◽  
Nearest Neighbor Algorithm
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