Linear Discriminant Analysis (LDA) is a well-known scheme for feature extraction and dimension reduction. It has been used widely in many applications involvinghigh-dimensional data, such as face recognition and image retrieval. An intrinsic limitation of classical LDA is the so-called singularity problem, that is, it fails when all scatter matrices are singular. Awell-known approach to deal with the singularity problem is to apply an intermediate dimension reduction stage using Principal Component Analysis(PCA) before LDA. The algorithm, called PCA LDA, is used widely in face recognition. However, PCA LDA has high costs in time and space, due to the need for an eigen-decomposition involving the scatter matrices. In this paper, we propose a novel LDA algorithm, namely 2DLDA, which stands for 2-Dimensional Linear Discriminant Analysis.

In this paper, we use the rollout method for policy improvement to analyze aversion of Klondike solitaire. This version, sometimes called thoughtful solitaire, has all cards revealed to the player, but then follows the usual Klondike rules. A strategy that we establish, using iterated rollouts, winsabout twice as many games on average as an expert human player does.

Linear Discriminant Analysis (LDA) is a well-known method for feature extraction and dimension reduction.

We study gender discrimination of human faces using a combination of psychophysical classification and discrimination experiments together with methods from machine learning. We reduce the dimensionality of a set of face images using principal component analysis, and then train a set of linear classifiers on this reduced representation (linear support vector machines(SVMs), relevance vector machines (RVMs), Fisher linear discriminant (FLD), and prototype (prot) classifiers) using human classification data.Because we combine a linear preprocessor with linear classifiers, the entire system acts as a linear classifier, allowing us to visualise thedecision-image corresponding to the normal vector of the separating hyperplanes(SH) of each classifier. We predict that the female-tomaleness transitionalong the normal vector for classifiers closely mimicking human classification (SVM and RVM [1]) should be faster than the transition along any other direction. A psychophysical discrimination experimentusing the decision images as stimuli is consistent with this prediction.

Recently, there have been several advances in the machine learning and pattern recognition communities for developing manifold learning algorithms toconstruct nonlinear low-dimensional manifolds from sample data points embedded in high-dimensional spaces. In this paper, we develop algorithmsthat address two key issues in manifold learning: 1) the adaptive selection of the neighborhood sizes; and 2) better fitting the local geometric structure to account for the variations in the curvature of the manifold and its interplay with the sampling density of the data set. We also illustrate the effectiveness of our methods on some synthetic data sets.

Many cellular functions are carried out through physical interactions between proteins. Discovering the protein interaction map can therefore help to better understand the workings of the cell.

We formulate the problem of graph inference where part of the graph is known as a supervised learning problem, and propose an algorithm to solve it. The method involves the learning of a mapping of the vertices to a Euclidean space where the graph is easy to infer, and can be formulated asan optimization problem in a reproducing kernel Hilbert space. We report encouraging results on the problem of metabolic network reconstruction fromgenomic data.

We address the problem of learning a symmetric positive definite matrix. The central issue is to design parameter updates that preserve positive definiteness. Our updates are motivated with the von Neumann divergence. Ratherthan treating the most general case, we focus on two key applications that exemplify our methods: Online learning with a simple square loss and finding a symmetric positive definite matrix subject to symmetric linear constraints. The updates generalize the Exponentiated Gradient (EG) update and AdaBoost, respectively: the parameter is now a symmetric positive definite matrix of trace one instead of a probability vector (which in this context is a diagonal positive definite matrix with trace one). The generalized updates use matrix logarithms and exponentials topreserve positive definiteness. Most importantly, we show how the analysis of each algorithm generalizes to the non-diagonal case. We apply both new algorithms, called the Matrix Exponentiated Gradient (MEG) update and DefiniteBoost, to learn a kernel matrix from distance measurements.

This paper explores the computational consequences of simultaneous intrinsic andsynaptic plasticity in individual model neurons. It proposes a new intrinsic plasticity mechanism for a continuous activation model neuron based on low order moments of the neuron's firing rate distribution. Thegoal of the intrinsic plasticity mechanism is to enforce a sparse distribution of the neuron's activity level. In conjunction with Hebbian learning at the neuron's synapses, the neuron is shown to discover sparse directions in the input.

The game of G0 originated in China over 4000 years ago.