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- Publications
- Influence

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 to… Expand

Distance Metric Learning for Large Margin Nearest Neighbor Classification

- Kilian Q. Weinberger, L. Saul
- Computer Science, Mathematics
- NIPS
- 5 December 2005

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 Mahalanobis… Expand

Think Globally, Fit Locally: Unsupervised Learning of Low Dimensional Manifold

The problem of dimensionality reduction arises in many fields of information processing, including machine learning, data compression, scientific visualization, pattern recognition, and neural… Expand

An Introduction to Variational Methods for Graphical Models

- Michael I. Jordan, Zoubin Ghahramani, T. Jaakkola, L. Saul
- Computer Science, Mathematics
- Machine Learning
- 1 February 1999

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 of… Expand

Beyond blacklists: learning to detect malicious web sites from suspicious URLs

- Justin Ma, L. Saul, S. Savage, G. Voelker
- Computer Science
- KDD
- 28 June 2009

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 this… Expand

Kernel Methods for Deep Learning

- Youngmin Cho, L. Saul
- Computer Science
- NIPS
- 7 December 2009

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 as… Expand

Learning a kernel matrix for nonlinear dimensionality reduction

- Kilian Q. Weinberger, F. Sha, L. Saul
- Computer Science
- ICML '04
- 4 July 2004

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 feature… Expand

Unsupervised Learning of Image Manifolds by Semidefinite Programming

- Kilian Q. Weinberger, L. Saul
- Computer Science
- CVPR
- 2004

Identifying suspicious URLs: an application of large-scale online learning

- Justin Ma, L. Saul, S. Savage, G. Voelker
- Computer Science
- ICML '09
- 14 June 2009

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 this… Expand

Fast solvers and efficient implementations for distance metric learning

- Kilian Q. Weinberger, L. Saul
- Computer Science
- ICML '08
- 5 July 2008

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 large… Expand