In this paper, an efficient local appearance feature extraction method based the multi-resolution Curvelet transform is proposed in order to further enhance the performance of the well known Linear Discriminant Analysis(LDA) method when applied to face recognition. Each face is described by a subset of band filtered images containing block-based Curvelet coefficients. These coefficients charact...

Linear discriminant analysis (LDA) is one of the most effective feature extraction methods in statistical pattern recognition, which extracts the discriminant features by maximizing the so-called Fisher’s criterion that is defined as the ratio of between-class scatter matrix to within-class scatter matrix. However, classification of high-dimensional statistical data is usually not amenable to s...

In this paper, we investigate the accuracy of linear classification techniques for a P300 Brain-Computer Interface used in a typing paradigm. Fisher’s Linear Discriminant Analysis (LDA), Bayesian Linear Discriminant Analysis (BLDA), Stepwise Linear Discriminant Analysis (SLDA), linear Support Vector Machine (SVM) and a method based on Feature Extraction (FE) were compared. Experiments were perf...

Facial feature extraction with enhanced discriminatory power plays an important role in face recognition (FR) applications. Linear discriminant analysis (LDA) is a powerful tool used for dimensionality reduction and feature extraction in FR tasks. However, the classification performance of traditional LDA is often degraded, due to two factors: 1) their classification accuracies suffer from the ...

Analysing the face recognition rate of various current face recognition algorithms is absolutely critical in developing new robust algorithms. In his paper we report performance analysis of Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) for face recognition. This analysis was carried out on various current PCA and LDA based face recognition algorithms using standard p...

Linear discriminant analysis (LDA) has been an active topic of research during the last century. However, the existing algorithms have several limitations when applied to visual data. LDA is only optimal for gaussian distributed classes with equal covariance matrices and just classes-1 features can be extracted. On the other hand, LDA does not scale well to high dimensional data (over-fitting) ...

Kernel trick is a powerful tool being used for solving complex pattern classification problem. As long as a linear feature extraction algorithm can be expressed exclusively by dot-products, it can be extended to non-linear version by combining kernel method. In this paper, we present such an improved iterative algorithm used for linear discriminant analysis. By mapping data onto high dimensiona...

A deep neural network (DNN) consists of a nonlinear transformation from an input to a feature representation, followed by a common softmax linear classifier. Though many efforts have been devoted to designing a proper architecture for nonlinear transformation, little investigation has been done on the classifier part. In this paper, we show that a properly designed classifier can improve robust...

1. student research committee, urmia university of medical sciences, urmia, iran 2. dept. of medical physics, faculty of medicine, urmia university of medical sciences, urmia, iran 3. dept. of radiology, faculty of medicine, imam khomeini hospital, urmia university of medical sciences, urmia, iran                             corresponding author: akbar gharbali, phd; assistant professor of medi...

Fisher linear discriminant analysis (LDA) can be sensitive to the problem data. Robust Fisher LDA can systematically alleviate the sensitivity problem by explicitly incorporating a model of data uncertainty in a classification problem and optimizing for the worst-case scenario under this model. The main contribution of this paper is show that with general convex uncertainty models on the proble...