This paper presents a modification of kernel-based Fisher discriminant analysis (FDA) to design one-class classifier for face detection. In face detection, it is reasonable to assume “face” images to cluster in certain way, but “non face” images usually do not cluster since different kinds of images are included. It is difficult to model “non face” images as a single distribution in the discrim...

Kernel-based methods have been of wide concern in the ,eld of machine learning and neurocomputing. In this paper, a new Kernel Fisher discriminant analysis (KFD) algorithm, called complete KFD (CKFD), is developed. CKFD has two advantages over the existing KFD algorithms. First, its implementation is divided into two phases, i.e., Kernel principal component analysis (KPCA) plus Fisher linear di...

Abstract Compositional data, i.e. data including only relative information, need to be transformed prior to applying the standard discriminant analysis methods that are designed for the Euclidean space. Here it is investigated for linear, quadratic, and Fisher discriminant analysis, which of the transformations lead to invariance of the resulting discriminant rules. Moreover, it is shown that f...

Many applications in signal processing need an adaptive algorithm. Adaptive schemes are useful when the statistics of the problem are unknown or when facing varying environments. Nonetheless, many of these applications deal with classification tasks, and most algorithms are not specifically thought to tackle these kinds of problems. Whereas Fisher’s criterion aimed to find the most adequate dir...

Mika et al. (in: Neural Network for Signal Processing, Vol. IX, IEEE Press, New York, 1999; pp. 41–48) apply the “kernel trick” to obtain a non-linear variant of Fisher’s linear discriminant analysis method, demonstrating state-of-the-art performance on a range of benchmark data sets. We show that leave-one-out cross-validation of kernel Fisher discriminant classi'ers can be implemented with a ...

A reformative kernel algorithm, which can deal with two-class problems as well as those with more than two classes, on Fisher discriminant analysis is proposed. In the novel algorithm the supposition that in feature space discriminant vector can be approximated by some linear combination of a part of training samples, called “signi6cant nodes”, is made. If the “signi6cant nodes” are found out, ...

Marginal Fisher Analysis (MFA) is a novel dimensionality reduction algorithm. However, the two nearest neighborhood parameters are difficult to select when constructing graphs. In this paper, we propose a nonparametric method called Marginal Discriminant Projection (MDP) which can solves the problem of parameters selection in MFA. Experiment on several benchmark datasets demonstrated the effect...

A reformative kernel Fisher discriminant method is proposed, which is directly derived from the naive kernel Fisher discriminant analysis with superiority in classi1cation e2ciency. In the novel method only a part of training patterns, called “signi1cant nodes”, are necessary to be adopted in classifying one test pattern. A recursive algorithm for selecting “signi1cant nodes”, which is the key ...