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Improving Regression Estimation: Averaging Methods for Variance Reduction with Extensions to General Convex Measure Optimization
, 1993
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Learning as Extraction of LowDimensional Representations
 Mechanisms of Perceptual Learning
, 1996
"... Psychophysical findings accumulated over the past several decades indicate that perceptual tasks such as similarity judgment tend to be performed on a lowdimensional representation of the sensory data. Low dimensionality is especially important for learning, as the number of examples required for a ..."
Abstract

Cited by 26 (7 self)
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Psychophysical findings accumulated over the past several decades indicate that perceptual tasks such as similarity judgment tend to be performed on a lowdimensional representation of the sensory data. Low dimensionality is especially important for learning, as the number of examples required for attaining a given level of performance grows exponentially with the dimensionality of the underlying representation space. In this chapter, we argue that, whereas many perceptual problems are tractable precisely because their intrinsic dimensionality is low, the raw dimensionality of the sensory data is normally high, and must be reduced by a nontrivial computational process, which, in itself, may involve learning. Following a survey of computational techniques for dimensionality reduction, we show that it is possible to learn a lowdimensional representation that captures the intrinsic lowdimensional nature of certain classes of visual objects, thereby facilitating further learning of tasks...
Discovering Predictable Classifications
 Neural Computation
, 1992
"... Prediction problems are among the most common learning problems for neural networks (e.g. in the context of time series prediction, control, etc.). With many such problems, however, perfect prediction is inherently impossible. For such cases we present novel unsupervised systems that learn to clas ..."
Abstract

Cited by 19 (10 self)
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Prediction problems are among the most common learning problems for neural networks (e.g. in the context of time series prediction, control, etc.). With many such problems, however, perfect prediction is inherently impossible. For such cases we present novel unsupervised systems that learn to classify patterns such that the classifications are predictable while still being as specific as possible. The approach can be related to the IMAX method of Hinton, Becker and Zemel (1989, 1991). Experiments include Becker's and Hinton's stereo task, which can be solved more readily by our system. 1 1 MOTIVATION AND BASIC APPROACH Many neural net systems (e.g. for control, time series prediction, etc.) rely on adaptive submodules for learning to predict patterns from other patterns. Perfect prediction, however, is often inherently impossible. In this paper we study the problem of finding pattern classifications such that the classes are predictable, while still being as specific as possibl...
Combining Exploratory Projection Pursuit And Projection Pursuit Regression With Application To Neural Networks
 Neural Computation
, 1992
"... We present a novel classification and regression method that combines exploratory projection pursuit (unsupervised training) with projection pursuit regression (supervised training), to yield a new family of cost/complexity penalty terms. Some improved generalization properties are demonstrated on r ..."
Abstract

Cited by 17 (9 self)
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We present a novel classification and regression method that combines exploratory projection pursuit (unsupervised training) with projection pursuit regression (supervised training), to yield a new family of cost/complexity penalty terms. Some improved generalization properties are demonstrated on real world problems. 1 Introduction Parameter estimation becomes difficult in highdimensional spaces due to the increasing sparseness of the data. Therefore, when a low dimensional representation is embedded in the data, dimensionality reduction methods become useful. One such method  projection pursuit regression (Friedman and Stuetzle, 1981) (PPR) is capable of performing dimensionality reduction by composition, namely, it constructs an approximation to the desired response function using a composition of lower dimensional smooth functions. These functions depend on low dimensional projections through the data. When the dimensionality of the problem is in the thousands, even projection...
Discriminative Clustering by Regularized Information Maximization
"... Is there a principled way to learn a probabilistic discriminative classifier from an unlabeled data set? We present a framework that simultaneously clusters the data and trains a discriminative classifier. We call it Regularized Information Maximization (RIM). RIM optimizes an intuitive information ..."
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Cited by 11 (1 self)
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Is there a principled way to learn a probabilistic discriminative classifier from an unlabeled data set? We present a framework that simultaneously clusters the data and trains a discriminative classifier. We call it Regularized Information Maximization (RIM). RIM optimizes an intuitive informationtheoretic objective function which balances class separation, class balance and classifier complexity. The approach can flexibly incorporate different likelihood functions, express prior assumptions about the relative size of different classes and incorporate partial labels for semisupervised learning. In particular, we instantiate the framework to unsupervised, multiclass kernelized logistic regression. Our empirical evaluation indicates that RIM outperforms existing methods on several real data sets, and demonstrates that RIM is an effective model selection method. 1
Unsupervised Learning for Boltzmann Machines
"... An unsupervised learning algorithm for a stochastic recurrent neural network based on the Boltzmann Machine architecture is formulated in this paper. The maximization of the Mutual Information between the stochastic output neurons and the clamped inputs is used as an unsupervised criterion for train ..."
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An unsupervised learning algorithm for a stochastic recurrent neural network based on the Boltzmann Machine architecture is formulated in this paper. The maximization of the Mutual Information between the stochastic output neurons and the clamped inputs is used as an unsupervised criterion for training the network. The resulting learning rule contains two terms corresponding to Hebbian and antiHebbian learning. It is interesting that these two terms are weighted by the amount of information transmitted in the learning synapse, giving an informationtheoretic interpretation of the proportionality constant of Hebb's biological rule. The antiHebbian term, which can be interpreted as a forgetting function, supports the optimal coding. In this way, optimal nonlinear and recurrent implementation of data compression of boolean patterns are obtained. As an example, the encoder problem is simulated and trained in an unsupervised way in a one layer network. Compression of nonuniform distribut...
Communicated by Steven J. Nowlan Combining Exploratory Projection Pursuit and Projection Pursuit Regression with Application to Neural Networks
"... We present a novel classification and regression method that combines exploratory projection pursuit (unsupervised training) with projection pursuit regression (supervised training), to yield a new family of costlcomplexity penalty terms. Some improved generalization properties are demonstrated on r ..."
Abstract
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We present a novel classification and regression method that combines exploratory projection pursuit (unsupervised training) with projection pursuit regression (supervised training), to yield a new family of costlcomplexity penalty terms. Some improved generalization properties are demonstrated on realworld problems. 1