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Nonlinear dimensionality reduction by locally linear embedding.
Many areas of science depend on exploratory data analysis and visualization. The need to analyze large amounts of multivariate data raises the fundamental problem of dimensionality reduction: how toExpand
  • 12,404
  • 1425
Distance Metric Learning for Large Margin Nearest Neighbor Classification
The accuracy of k-nearest neighbor (kNN) classification depends significantly on the metric used to compute distances between different examples. In this paper, we show how to learn a MahalanobisExpand
  • 4,184
  • 631
Think Globally, Fit Locally: Unsupervised Learning of Low Dimensional Manifold
  • L. Saul, S. Roweis
  • Computer Science, Mathematics
  • J. Mach. Learn. Res.
  • 1 December 2003
The problem of dimensionality reduction arises in many fields of information processing, including machine learning, data compression, scientific visualization, pattern recognition, and neuralExpand
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An Introduction to Variational Methods for Graphical Models
This paper presents a tutorial introduction to the use of variational methods for inference and learning in graphical models (Bayesian networks and Markov random fields). We present a number ofExpand
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  • 128
Beyond blacklists: learning to detect malicious web sites from suspicious URLs
Malicious Web sites are a cornerstone of Internet criminal activities. As a result, there has been broad interest in developing systems to prevent the end user from visiting such sites. In thisExpand
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Kernel Methods for Deep Learning
We introduce a new family of positive-definite kernel functions that mimic the computation in large, multilayer neural nets. These kernel functions can be used in shallow architectures, such asExpand
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Learning a kernel matrix for nonlinear dimensionality reduction
We investigate how to learn a kernel matrix for high dimensional data that lies on or near a low dimensional manifold. Noting that the kernel matrix implicitly maps the data into a nonlinear featureExpand
  • 493
  • 45
Identifying suspicious URLs: an application of large-scale online learning
This paper explores online learning approaches for detecting malicious Web sites (those involved in criminal scams) using lexical and host-based features of the associated URLs. We show that thisExpand
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  • 44
Fast solvers and efficient implementations for distance metric learning
In this paper we study how to improve nearest neighbor classification by learning a Mahalanobis distance metric. We build on a recently proposed framework for distance metric learning known as largeExpand
  • 268
  • 44