Pairwise Nonparametric Discriminant Analysis for Binary Plankton Image Recognition

2014 ◽  
Vol 39 (4) ◽  
pp. 695-701 ◽  
Author(s):  
Zhifeng Li ◽  
Feng Zhao ◽  
Jianzhuang Liu ◽  
Yu Qiao
Keyword(s):  
Discriminant Analysis ◽  
Image Recognition ◽  
Nonparametric Discriminant Analysis

scholarly journals Local kernel nonparametric discriminant analysis for adaptive extraction of complex structures

Open Physics ◽  
2017 ◽  
Vol 15 (1) ◽  
pp. 270-279
Author(s):  
Quanbao Li ◽  
Fajie Wei ◽  
Shenghan Zhou
Keyword(s):  
Feature Extraction ◽  
Data Structure ◽  
Discriminant Analysis ◽  
Ad Hoc ◽  
Adaptive Method ◽  
Local Data ◽  
Linear Discriminant ◽  
Linear Feature ◽  
Nonparametric Discriminant Analysis ◽  
Nonlinear Feature Extraction

AbstractThe linear discriminant analysis (LDA) is one of popular means for linear feature extraction. It usually performs well when the global data structure is consistent with the local data structure. Other frequently-used approaches of feature extraction usually require linear, independence, or large sample condition. However, in real world applications, these assumptions are not always satisfied or cannot be tested. In this paper, we introduce an adaptive method, local kernel nonparametric discriminant analysis (LKNDA), which integrates conventional discriminant analysis with nonparametric statistics. LKNDA is adept in identifying both complex nonlinear structures and the ad hoc rule. Six simulation cases demonstrate that LKNDA have both parametric and nonparametric algorithm advantages and higher classification accuracy. Quartic unilateral kernel function may provide better robustness of prediction than other functions. LKNDA gives an alternative solution for discriminant cases of complex nonlinear feature extraction or unknown feature extraction. At last, the application of LKNDA in the complex feature extraction of financial market activities is proposed.

scholarly journals Semi-Supervised Nonparametric Discriminant Analysis

2013 ◽  
Vol E96.D (2) ◽  
pp. 375-378 ◽  
Author(s):  
Xianglei XING ◽  
Sidan DU ◽  
Hua JIANG
Keyword(s):  
Discriminant Analysis ◽  
Nonparametric Discriminant Analysis

FAST CLUSTERING-BASED KERNEL FOLEY–SAMMON TRANSFORM

2009 ◽  
Vol 07 (01) ◽  
pp. 75-87 ◽  
Author(s):  
WEI LU ◽  
ZHENZHOU CHEN ◽  
ZHENGAN YAO ◽  
LEI LI
Keyword(s):  
Discriminant Analysis ◽  
Sample Size ◽  
Image Recognition ◽  
Kernel Method ◽  
Original Data ◽  
Experimental Results ◽  
Kernel Matrix ◽  
Clustering Data ◽  
Large Kernel ◽  
Matrix Calculation

The Kernel Foley–Sammon Transform (KFST) performs well in solving nonlinear discriminant analysis problems. However, as a kernel method, KFST also faces with large kernel matrix calculation problems O(n3) with the sample size n. KFST will be very costly and even intractable to compute when n is large. In this paper, we propose a Fast Clustering-based Kernel Foley–Sammon Transform (FCKFST) approach to tackle this problem. FCKFST solves KFST over a reductive l × l matrix representing clustering data instead of an n × n matrix of the original data, where l is exceedingly smaller than n. This paper also proves that FCKFST improving the calculating efficiency does not decrease the classification precision with comparison to KFST. We apply our method to digit and image recognition problems, and we obtain good experimental results.

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