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

In this paper an ensemble feature extraction algorithm is proposed based on Adaboost.M2 for multiclass classification problem. The proposed algorithm makes no assumption about the distribution of the data and primarily performs by directly selecting the discriminant features with the minimum training error, which can overcome the main drawbacks of the traditional methods, such as Principle Comp...

A class of image-matrix-based feature extraction algorithms has been discussed earlier. The correspondence argues that 2-D principal component analysis and Fisher linear discriminant (FLD) are equivalent to block-based PCA and FLD. In this correspondence, we point out that this statement is not rigorous.

This paper proposes an innovative algorithm named 2D-LDA, which directly extracts the proper features from image matrices based on Fisher s Linear Discriminant Analysis. We experimentally compare 2D-LDA to other feature extraction methods, such as 2D-PCA, Eigenfaces and Fisherfaces. And 2D-LDA achieves the best performance. 2004 Elsevier B.V. All rights reserved.

|The Fisher{Rao linear discriminant analysis (LDA) is a valuable tool for multi-class clas-siication and data reduction. We investigate LDA within the maximum likelihood framework and propose a general formulation to handle heteroscedastic-ity. Small size numerical experiments with randomly generated data verify the validity of our formulation.

Computing linear classi ers is a very important problem in statistical Pattern Recognition (PR). These classi ers have been investigated by the PR community extensively since they are the ones which are both easy to implement and comprehend. It is well known that when dealing with normally distributed classes, the optimal discriminant function for two-classes is linear only when the covariance ...

This paper focuses on the problem of a robust estimation of different transformation matrices based on the well known linear discriminant analysis (LDA) as it is used in automatic speech recognition systems. We investigate the effect of class distributions with artificial features and compare the resulting Fisher criterion. This paper shows that it is not very helpful to use only the Fisher cri...

Person re-identification (re-id) is to match people across disjoint camera views in a multi-camera system, and re-id has been an important technology applied in smart city in recent years. However, the majority of existing person re-id methods are not designed for processing sequential data in an online way. This ignores the real-world scenario that person images detected from multi-cameras sys...

Over the past decades, Floating Offshore Wind Turbine (FOWT) has gained increasing attention in wind engineering due to rapidly growing energy demands. However, difficulties turbine maintenance will increase harsh operational conditions. Fault diagnosis techniques play a crucial role enhance reliability of FOWTs and reduce cost offshore energy. In this paper, novel data-driven fault method usin...