We present a general method using kernel Canonical Correlation Analysis to learn a semantic representation to web images and their associated text. The semantic space provides a common representation and enables a comparison between the text and images. In the experiments we look at two approaches of retrieving images based only on their content from a text query. We compare the approaches against a standard cross-representation retrieval technique known as the Generalised Vector Space Model.

Fisher's linear discriminant analysis (LDA) is a popular data-analytic tool for studying the relationship between a set of predictors and a categorical response. In this paper we describe a penalized version of LDA. It is designed for situations in which there are many highly correlated predictors, such as those obtained by discretizing a function, or the greyscale values of the pixels in a series of images. In cases such as these it is natural, efficient, and sometimes essential to impose a spatial smoothness constraint on the coefficients, both for improved prediction performance and interpretability. We cast the classification problem into a regression framework via optimal scoring. Using this, our proposal facilitates the use of any penalized regression technique in the classification setting. The technique is illustrated with examples in speech recognition and handwritten character recognition. AMS 1991 Classifications: Primary 62H30, Secondary 62G07 1 Introduction Linear discrim...

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There has been much publicity about the ability of artificial neural networks to learn and generalize. In fact, the most commonly used artificial neural networks, called multilayer perceptrons, are nothing more than nonlinear regression and discriminant models that can be implemented with standard statistical software. This paper explains what neural networks are, translates neural network jargon into statistical jargon, and shows the relationships between neural networks and statistical models such as generalized linear models, maximum redundancy analysis, projection pursuit, and cluster analysis.

this paper we discuss four such classes of algorithms. Or, more precisely, we discuss a single class of algorithms, and we show how some well-known classes of statistical algorithms fit in this common class. The subclasses are, in logical order, block-relaxation methods augmentation methods majorization methods Expectation-Maximization Alternating Least Squares Alternating Conditional Expectations

The number of knowledge-based systems that build on Bayesian belief networks is increasing. The construction of such a network however requires a large number of probabilities in numerical form. This is often considered a major obstacle, one of the reasons being that experts are reluctant to provide numerical probabilities. The use of verbal probability expressions as an additional method of eliciting probabilistic information may to some extent remove this obstacle. In this paper, we review studies that address the communication of probabilities in words and/or numbers. We then describe our own experiments concerning the development of a probability scale that contains words as well as numbers. This scale appears to be an aid for researchers and domain experts during the elicitation phase of building a belief network and might help users understand the output of the network.

is generally meant by data mining nowadays. Statistics and data mining have much in common, but they also have differences. The nature of the two disciplines is examined, with emphasis on their similarities and differences.

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The Gifi system of analyzing categorical data through nonlinear varieties of classical multivariate analysis techniques is reviewed. The system is characterized by the optimal scaling of categorical variables which is implemented through alternating least squares algorithms. The main technique of homogeneity analysis is presented, along with its extensions and generalizations leading to nonmetric principal components analysis and canonical correlation analysis. A brief account of stability issues and areas of applications of the techniques is also given.

INTRODUCTION Consider a multivariate random variable X in R p with density function f and a random sample from X, namely X 1 , ..., X n . The first principal component can be viewed as the straight line which best fits the cloud of data (see, e.g., [17, pp. 386#387]). When the distribution of X is ellipsoidal the population first principal component is the main axis of the ellipsoids of equal concentration. In the past 40 years many works have appeared proposing extensions of principal components to distributions with nonlinear structure. We cite Shepard and Carroll [24], Gnanadesikan and Wilk [13], Srivastava [27], Etezadi-Amoli and McDonald [10], Yohai, Ackermann and Haigh [33], Koyak [19] and Gifi [12], among others. Some of them look for nonlinear transformations of the observable variables into spaces admitting a doi:10