Aiming at deficiencies of the ability for preserving local nonlinear structure of recently proposed Regularized Orthogonal Linear Discriminant Analysis (ROLDA) for dimensionality reduction, a kind of dimensionality reduction algorithm named Regularized Orthogonal Local Fisher Discriminant Analysis (ROLFDA) is proposed in the paper, which is originated from ROLDA. The algorithm introduce the ide...

A random vector x arises from one of two multivariate normal distributions differing in mean but not covariance. A training set xl, X2, X*, of previous cases, along with their correct assignments, is known. These can be used to estimate Fisher's discriminant by maximum likelihood and then to assign x on the basis of the estimated discriminant, a method known as the normal discrimination procedu...

This paper extends previous work in discriminant analysis with von Mises-Fisher distributions (e. g., Morris and Laycock, Biometrika, 1974) to general dimension, allowing computation of misclassification probabilities. The main result is the probability distribution of the cosine transformation of a von Mises-Fisher distribution, that is, the random variable , where , satisfying , is a random d...

This work proposes a method which enables us to perform kernel Fisher discriminant analysis in the whole eigenspace for face recognition. It employs the ratio of eigenvalues to decompose the entire kernel feature space into two subspaces: a reliable subspace spanned mainly by the facial variation and an unreliable subspace due to finite number of training samples. Eigenvectors are then scaled u...

We derive a novel sparse version of Kernel Fisher Discriminant Analysis (KFDA) using an approach based on Matching Pursuit (MP). We call this algorithm Matching Pursuit Kernel Fisher Discriminant Analysis (MPKFDA). We provide generalisation error bounds analogous to those constructed for the Robust Minimax algorithm together with a sample compression bounding technique. We present experimental ...

We propose in this paper how Fisher discriminant analysis can be applied for differentiating classes of semiconductor data from different tools or chambers. The tool and chamber matching analysis can be useful not only from a process characterization standpoint, but also for identifying the proper fault detection and classification strategy. If the FDA analysis shows that the chambers or tools ...

There has been much recent attention to the problem of learning an appropriate distance metric, using class labels or other side information. Some proposed algorithms are iterative and computationally expensive. In this paper, we show how to solve one of these methods with a closed-form solution, rather than using semidefinite programming. We provide a new problem setup in which the algorithm p...