Linear Discriminant Analysis(LDA) is well-known scheme for feature extraction and dimension reduction. It has been used widely in many applications involving high-dimensional data, such as face recognition. In this paper we present a new variant on Linear Discriminant Analysis (LDA) for face recognition by reducing dimensions of input data using matrix representation and after that using singul...

In Linear Discriminant Analysis (LDA), a dimension reducing linear transformation is found in order to better distinguish clusters from each other in the reduced dimensional space. However, LDA has a limitation that one of the scatter matrices is required to be nonsingular and the nonlinearly clustered structure is not easily captured. We propose a nonlinear discriminant analysis based on kerne...

Fish as aquatic animals have several physiological mechanisms that land do not have. Differences in habitat cause fish to adapt environmental conditions, for example live water, both fresh and marine waters. The number of species or types freshwater means knowledge the fish. Identification images is useful community, because different nutritional content, prices processing each type. Likewise c...

A new LDA-based face recognition system is presented in this paper. Linear discriminant analysis (LDA) is one of the most popular linear projection techniques for feature extraction. The major drawback of applying LDA is that it may encounter the small sample size problem. In this paper, we propose a new LDA-based technique which can solve the small sample size problem. We also prove that the m...

Linear Discriminant Analysis (LDA) is a dimension reduction method which finds an optimal linear transformation that maximizes the class separability. However, in undersampled problems where the number of data samples is smaller than the dimension of data space, it is difficult to apply the LDA due to the singularity of scatter matrices caused by high dimensionality. In order to make the LDA ap...

Fishers linear discriminant analysis (LDA) is a classical multivariate technique both for dimension reduction and classification. The data vectors are transformed into a low dimensional subspace such that the class centroids are spread out as much as possible. In this subspace LDA works as a simple prototype classifier with linear decision boundaries. However, in many applications the linear bo...

The classification of upper-limb movements based on surface electromyography (EMG) signals is an important issue in the control of assistive devices and rehabilitation systems. Increasing the number of EMG channels and features in order to increase the number of control commands can yield a high dimensional feature vector. To cope with the accuracy and computation problems associated with high ...

In this paper, we propose a new discriminant analysis using composite features for pattern classification. A composite feature consists of a number of primitive features, each of which corresponds to an input variable. The covariance of composite features is obtained from the inner product of composite features and can be considered as a generalized form of the covariance of primitive features....

In this paper, a relationship between Linear Discriminant Analysis (LDA) and the generalized Minimum Squared Error (MSE) solution is presented. The generalized MSE solution is shown to be equivalent to applying a certain classification rule in the space defined by LDA. The relationship between the MSE solution and Fisher Discriminant Analysis (FDA) is extended to multi-class problems and also t...

In this paper, a relationship between linear discriminant analysis (LDA) and the generalized minimum squared error (MSE) solution is presented. The generalized MSE solution is shown to be equivalent to applying a certain classification rule in the space defined by LDA. The relationship between the MSE solution and Fisher discriminant analysis is extended to multiclass problems and also to under...