Simultaneous model-based clustering and visualization in the Fisher discriminative subspace

Clustering in high-dimensional spaces is nowadays a recurrent problem in many scientific domains but remains a difficult task from both the clustering accuracy and the result understanding points of view. This paper presents a discriminative latent mixture (DLM) model which fits the data in a latent orthonormal discriminative subspace with an intrinsic dimension lower than the dimension of the original space. By constraining model parameters within and between groups, a family of 12 parsimonious DLM models is exhibited which allows to fit onto various situations. An estimation algorithm, called the Fisher-EM algorithm, is also proposed for estimating both the mixture parameters and the discriminative subspace. Experiments on simulated and real datasets show that the proposed approach performs better than existing clustering methods while providing a useful representation of the clustered data. The method is as well applied to the clustering of mass spectrometry data

Independent component analysis as a rotation method: A very different solution to Thurstone's box problem

In this paper we consider the well-known Thurstone box problem in exploratory factor analysis. Initial loadings and components are extracted using principal component analysis. Rotating the components towards independence rather than rotating the loadings towards simplicity allows one to accurately recover the dimensions of each box and also produce simple loadings. It is shown how this may be done using an appropriate rotation criterion and a general rotation algorithm. Methods from independent component analysis are used, and this paper may be viewed as an introduction to independent component analysis from the perspective of factor analysis

Independent component analysis of climate data: a new look at EOF rotation

The complexity inherent in climate data makes it necessary to introduce more than one statistical tool to the researcher to gain insight into the climate system. Empirical orthogonal function (EOF) analysis is one of the most widely used methods to analyze weather/climate modes of variability and to reduce the dimensionality of the system. Simple structure rotation of EOFs can enhance interpretability of the obtained patterns but cannot provide anything more than temporal uncorrelatedness. In this paper, an alternative rotation method based on independent component analysis (ICA) is considered. The ICA is viewed here as a method of EOF rotation. Starting from an initial EOF solution rather than rotating the loadings toward simplicity, ICA seeks a rotation matrix that maximizes the independence between the components in the time domain. If the underlying climate signals have an independent forcing, one can expect to find loadings with interpretable patterns whose time coefficients have properties that go beyond simple noncorrelation observed in EOFs. The methodology is presented and an application to monthly means sea level pressure (SLP) field is discussed. Among the rotated (to independence) EOFs, the North Atlantic Oscillation (NAO) pattern, an Arctic Oscillation–like pattern, and a Scandinavian-like pattern have been identified. There is the suggestion that the NAO is an intrinsic mode of variability independent of the Pacific

Projected gradient approach to the numerical solution of the SCoTLASS

The SCoTLASS problem-principal component analysis modified so that the components satisfy the Least Absolute Shrinkage and Selection Operator (LASSO) constraint-is reformulated as a dynamical system on the unit sphere. The LASSO inequality constraint is tackled by exterior penalty function. A globally convergent algorithm is developed based on the projected gradient approach. The algorithm is illustrated numerically and discussed on a well-known data set. (c) 2004 Elsevier B.V. All rights reserved

Iterative majorizing rotation to orthogonal simple structure solution

An iterative process is proposed for obtaining an orthogonal simple structure solution. At each iteration step a target matrix is constructed, such that the relative contributions of the target majorize the original ones, factor by factor. The convergence of the procedure is proven. The algorithm is illustrated with several standard numerical examples. Simple structure solution is achieved for the Thurstone's 26-variable box problem

Scientific research complex for the study of human operator in extreme conditions

In paper is presented a research complex which will recreate as closely as possible the diversity of the real working environment with appropriate incentives and will register the necessary examination of physical, physiological signals and parameters. Apparatus complex will simulate close to real work situations, will include tests necessary to assess the psychological status of the research subject. Further ahead in the complex algorithm will monitor the overall conduct of the experiment will record and process information from all physiological channels: electro miograma, electrocardiogram, blood pressure, pulse wave, respiration rate, galvanic skin response. The operator will receive visual and auditory information from the computer complex and will communicate with him through a special keyboard, standard optical mouse, track ball, stick and pedal controls. It is proposed methodology for conducting comprehensive studies of operator in extreme conditions. The developed complex will contribute to enhancing the skills of operators of mission-critical systems - pilots, pilots, operators of unmanned aerial vehicles, astronauts, heads of air traffic, nuclear operators and drivers of public transport