scholarly journals Information Discriminant Analysis: Feature Extraction with an Information-Theoretic Objective

2007 ◽  
Vol 29 (8) ◽  
pp. 1394-1407 ◽  
Author(s):  
Zoran Nenadic
Keyword(s):  
Feature Extraction ◽  
Discriminant Analysis ◽  
Information Theoretic

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.

Classification of Sleep Stages Using Multi-wavelet Time Frequency Entropy and LDA

2010 ◽  
Vol 49 (03) ◽  
pp. 230-237 ◽  
Author(s):  
K. Lweesy ◽  
N. Khasawneh ◽  
M. Fraiwan ◽  
H. Wenz ◽  
H. Dickhaus ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Wavelet Transform ◽  
Discriminant Analysis ◽  
Sleep Stage ◽  
Continuous Wavelet ◽  
Eeg Signal ◽  
Linear Discriminant ◽  
Time Frequency ◽  
Mother Wavelets ◽  
Sleep Stage Scoring

Summary Background: The process of automatic sleep stage scoring consists of two major parts: feature extraction and classification. Features are normally extracted from the polysomno-graphic recordings, mainly electroencephalograph (EEG) signals. The EEG is considered a non-stationary signal which increases the complexity of the detection of different waves in it. Objectives: This work presents a new technique for automatic sleep stage scoring based on employing continuous wavelet transform (CWT) and linear discriminant analysis (LDA) using different mother wavelets to detect different waves embedded in the EEG signal. Methods: The use of different mother wave-lets increases the ability to detect waves in the EEG signal. The extracted features were formed based on CWT time frequency entropy using three mother wavelets, and the classification was performed using the linear discriminant analysis. Thirty-two data sets from the MIT-BIH database were used to evaluate the performance of the proposed method. Results: Features of a single EEG signal were extracted successfully based on the time frequency entropy using the continuous wavelet transform with three mother wavelets. The proposed method has shown to outperform the classification based on a CWT using a single mother wavelet. The accuracy was found to be 0.84, while the kappa coefficient was 0.78. Conclusions: This work has shown that wavelet time frequency entropy provides a powerful tool for feature extraction for the non-stationary EEG signal; the accuracy of the classification procedure improved when using multiple wavelets compared to the use of single wavelet time frequency entropy.

Image feature extraction via local tensor rank one discriminant analysis

2011 ◽  
Vol 47 (24) ◽  
pp. 1320
Author(s):  
S.-S. Wu ◽  
Z.-S. Wei ◽  
J.-F. Lu ◽  
J.-Y. Yang
Keyword(s):  
Feature Extraction ◽  
Discriminant Analysis ◽  
Image Feature ◽  
Tensor Rank ◽  
Image Feature Extraction ◽  
Rank One

scholarly journals Pengenalan Wajah Menggunakan Metode Linear Discriminant Analysis dan k Nearest Neighbor

2017 ◽  
Vol 9 (1) ◽  
pp. 1-9
Author(s):  
Fandiansyah Fandiansyah ◽  
Jayanti Yusmah Sari ◽  
Ika Putri Ningrum
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Nearest Neighbor ◽  
Experimental Result ◽  
K Nearest Neighbor ◽  
Linear Discriminant ◽  
Nearest Neighbor Classifier ◽  
Neighbor Classifier

Face recognition is one of the biometric system that mostly used for individual recognition in the absent machine or access control. This is because the face is the most visible part of human anatomy and serves as the first distinguishing factor of a human being. Feature extraction and classification are the key to face recognition, as they are to any pattern classification task. In this paper, we describe a face recognition method based on Linear Discriminant Analysis (LDA) and k-Nearest Neighbor classifier. LDA used for feature extraction, which directly extracts the proper features from image matrices with the objective of maximizing between-class variations and minimizing within-class variations. The features of a testing image will be compared to the features of database image using K-Nearest Neighbor classifier. The experiments in this paper are performed by using using 66 face images of 22 different people. The experimental result shows that the recognition accuracy is up to 98.33%. Index Terms—face recognition, k nearest neighbor, linear discriminant analysis.

Chatter Recognition in Band Sawing Based on Feature Extraction and Discriminant Analysis

2012 ◽  
pp. 607-615 ◽  
Author(s):  
Tilen Thaler ◽  
Primož Potočnik ◽  
Peter Mužič ◽  
Ivan Bric ◽  
Rudi Bric ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Discriminant Analysis
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