scholarly journals A Comparison of Two Linear Discriminant Analysis Methods That Use Block Monotone Missing Training Data

2016 ◽  
Vol 06 (01) ◽  
pp. 172-185
Author(s):  
Phil D. Young ◽  
Dean M. Young ◽  
Songthip T. Ounpraseuth
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Training Data ◽  
Linear Discriminant ◽  
Analysis Methods

Parameterized Discriminant Analysis Methods

2011 ◽  
pp. 78-105
Author(s):  
David Zhang ◽  
Fengxi Song ◽  
Yong Xu ◽  
Zhizhen Liang
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Scatter Matrix ◽  
Linear Discriminant ◽  
Analysis Methods

In this chapter, we mainly present three kinds of weighted LDA methods. In Sections 5.1, 5.2 and 5.3, we respectively present parameterized direct linear discriminant analysis, weighted nullspace linear discriminant analysis and weighted LDA in the range of within-class scatter matrix. We offer a brief summery of the chapter in Section 5.4.

The Comparison of Laplacianfaces QR Decomposition and Linear Discriminant Analysis QR Decomposition Algorithm for Face Recognition System on Orthogonal Subspace

2014 ◽  
Vol 71 (1) ◽  
Author(s):  
Purbandini Purbandini
Keyword(s):  
Face Recognition ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Selection Process ◽  
Recognition System ◽  
Qr Decomposition ◽  
Training Data ◽  
Distance Method ◽  
Linear Discriminant ◽  
Face Recognition System

Development of an optimal face recognition system will greatly depend on the characteristics of the selection process are as a basis to pattern recognition. In the characteristic selection process, there are 2 aspects that will be of mutual influence such the reduction of the amount of data used in the classification aspects and increasing discrimination ability aspects. Linear Discriminat Analysis method helps presenting the global structure while Laplacianfaces method is one method that is based on appearance (appearance-based method) in face recognition, in which the local manifold structure presented in the adjacency graph mapped from the training data points. Linear Discriminant Analysis QR decomposition has a computationally low cost because it has small dimensions so that the efficiency and scalability are very high when compared with algorithms of other Linear Discriminant Analysis methods. Laplacianfaces QR decomposition was a algorithm to obtain highly speed and accuracy, and tiny space to keep data on the face recognition. This algorithm consists of 2 stages. The first stage maximizes the distance of between-class scatter matrices by using QR decomposition and the second stage to minimize the distance of within-class scatter matrices. Therefore, it is obtained an optimal discriminant in the data. In this research, classification using the Euclidean distance method. In these experiments using face databases of the Olivetti-Att-ORL, Bern and Yale. The minimum error was achieved with the Laplacianfaces QR decomposition and Linear Discriminant Analysis QR decomposition are 5.88% and 9.08% respectively. 

scholarly journals Automated detection of disulfide bridges in electron density maps using linear discriminant analysis

2005 ◽  
Vol 38 (1) ◽  
pp. 121-125 ◽  
Author(s):  
Thomas R. Ioerger
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Electron Density ◽  
Model Building ◽  
Computational Method ◽  
Training Data ◽  
Decision Threshold ◽  
Disulfide Bridges ◽  
Linear Discriminant ◽  
Density Maps

The ability to recognize disulfide bridges automatically in electron density maps would be useful to both protein crystallographers and automated model-building programs. A computational method is described for recognizing disulfide bridges in uninterpreted maps based on linear discriminant analysis. For each localized spherical region in a map, a vector of rotation-invariant numeric features is calculated that captures various aspects of the local pattern of density. These features values are then input into a linear equation, with coefficients computed to optimize discrimination of a set of training examples (disulfides and non-disulfides), and compared with a decision threshold. The method is shown to be highly accurate at distinguishing disulfides from non-disulfides in both the original training data and in real (experimental) electron density maps of other proteins.

scholarly journals Improving Covariance Matrices Derived from Tiny Training Datasets for the Classification of Event-Related Potentials with Linear Discriminant Analysis

2020 ◽  
Author(s):  
Jan Sosulski ◽  
Jan-Philipp Kemmer ◽  
Michael Tangermann
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Covariance Matrix ◽  
Event Related Potentials ◽  
Classification Performance ◽  
Event Related Potential ◽  
Feature Representation ◽  
Training Data ◽  
Linear Discriminant ◽  
Related Potentials

AbstractElectroencephalogram data used in the domain of brain–computer interfaces typically has subpar signal-to-noise ratio and data acquisition is expensive. An effective and commonly used classifier to discriminate event-related potentials is the linear discriminant analysis which, however, requires an estimate of the feature distribution. While this information is provided by the feature covariance matrix its large number of free parameters calls for regularization approaches like Ledoit–Wolf shrinkage. Assuming that the noise of event-related potential recordings is not time-locked, we propose to decouple the time component from the covariance matrix of event-related potential data in order to further improve the estimates of the covariance matrix for linear discriminant analysis. We compare three regularized variants thereof and a feature representation based on Riemannian geometry against our proposed novel linear discriminant analysis with time-decoupled covariance estimates. Extensive evaluations on 14 electroencephalogram datasets reveal, that the novel approach increases the classification performance by up to four percentage points for small training datasets, and gracefully converges to the performance of standard shrinkage-regularized LDA for large training datasets. Given these results, practitioners in this field should consider using our proposed time-decoupled covariance estimation when they apply linear discriminant analysis to classify event-related potentials, especially when few training data points are available.

scholarly journals Klasifikasi Kain Songket Lombok Berdasarkan Fitur GLCM dan Moment Invariant Dengan Teknik Pengklasifikasian Linear Discriminant Analysis (LDA)

2020 ◽  
Vol 2 (2) ◽  
pp. 173-183
Author(s):  
Nurhalimah Nurhalimah ◽  
I Gede Pasek Suta Wijaya ◽  
Fitri Bimantoro
Keyword(s):  
Feature Extraction ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Extraction Process ◽  
Woven Fabrics ◽  
Image Resolution ◽  
Training Data ◽  
Moment Invariant ◽  
Invariant Moment ◽  
Linear Discriminant

Songket is one of Indonesia's cultural heritage that is still present today. One of the most famous songket woven fabrics is the Lombok songket. Lombok songket has diverse, unique, and beautiful motifs. However public knowledge of Lombok songket motifs is still minimal and the difference between one motif with another is still unknown. The lack of digitalized data collection is one reason for this. Therefore, we need a system that can classify the Lombok songket automatically. In this study, a system was developed based on texture features and shape features using Linear Discriminant Analysis (LDA). The GLCM method is used in the texture feature extraction process and the Invariant Moment method is used in the feature extraction process. The total data used in this study is 1000 images from 10 Lombok songket motifs which are divided into training data and test data. The highest accuracy is obtained on the Invariant Moment and GLCM feature with an image resolution of 300x300 pixels using the most effective feature that is equal to 96.67%.

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