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105
Canonical correlation analysis; An overview with application to learning methods
, 2007
"... 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 o ..."
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Cited by 162 (14 self)
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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 crossrepresentation retrieval technique known as the Generalised Vector Space Model.
Penalized Discriminant Analysis
 Annals of Statistics
, 1995
"... Fisher's linear discriminant analysis (LDA) is a popular dataanalytic 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, ..."
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Cited by 131 (9 self)
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Fisher's linear discriminant analysis (LDA) is a popular dataanalytic 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...
Neural Networks and Statistical Models
, 1994
"... 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 s ..."
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Cited by 99 (1 self)
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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.
Blockrelaxation Algorithms in Statistics
, 1994
"... this paper we discuss four such classes of algorithms. Or, more precisely, we discuss a single class of algorithms, and we show how some wellknown classes of statistical algorithms fit in this common class. The subclasses are, in logical order, blockrelaxation methods augmentation methods majoriza ..."
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Cited by 29 (1 self)
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this paper we discuss four such classes of algorithms. Or, more precisely, we discuss a single class of algorithms, and we show how some wellknown classes of statistical algorithms fit in this common class. The subclasses are, in logical order, blockrelaxation methods augmentation methods majorization methods ExpectationMaximization Alternating Least Squares Alternating Conditional Expectations
Statistics and Data Mining: Intersecting Disciplines
 SIGKDD Explorations
, 1999
"... 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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Cited by 28 (1 self)
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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.
Talking Probabilities: Communicating Probabilistic Information With Words And Numbers
 International Journal of Approximate Reasoning
, 1999
"... The number of knowledgebased 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 ..."
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Cited by 27 (4 self)
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The number of knowledgebased 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.
Another Look at Principal Curves and Surfaces
, 2001
"... 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 e ..."
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Cited by 20 (2 self)
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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], EtezadiAmoli 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
The Gifi System Of Descriptive Multivariate Analysis
 STATISTICAL SCIENCE
, 1998
"... 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 h ..."
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Cited by 18 (3 self)
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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.
Towards comprehensive foundations of computational intelligence
 In: Duch W, Mandziuk J, Eds, Challenges for Computational Intelligence
, 2007
"... Abstract. Although computational intelligence (CI) covers a vast variety of different methods it still lacks an integrative theory. Several proposals for CI foundations are discussed: computing and cognition as compression, metalearning as search in the space of data models, (dis)similarity based m ..."
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Cited by 15 (12 self)
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Abstract. Although computational intelligence (CI) covers a vast variety of different methods it still lacks an integrative theory. Several proposals for CI foundations are discussed: computing and cognition as compression, metalearning as search in the space of data models, (dis)similarity based methods providing a framework for such metalearning, and a more general approach based on chains of transformations. Many useful transformations that extract information from features are discussed. Heterogeneous adaptive systems are presented as particular example of transformationbased systems, and the goal of learning is redefined to facilitate creation of simpler data models. The need to understand data structures leads to techniques for logical and prototypebased rule extraction, and to generation of multiple alternative models, while the need to increase predictive power of adaptive models leads to committees of competent models. Learning from partial observations is a natural extension towards reasoning based on perceptions, and an approach to intuitive solving of such problems is presented. Throughout the paper neurocognitive inspirations are frequently used and are especially important in modeling of the higher cognitive functions. Promising directions such as liquid and laminar computing are identified and many open problems presented. 1