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Nonlinear dimensionality reduction by locally linear embedding.
Locally linear embedding (LLE) is introduced, an unsupervised learning algorithm that computes low-dimensional, neighborhood-preserving embeddings of high-dimensional inputs that learns the global structure of nonlinear manifolds.
Distance Metric Learning for Large Margin Nearest Neighbor Classification
This paper shows how to learn a Mahalanobis distance metric for kNN classification from labeled examples in a globally integrated manner and finds that metrics trained in this way lead to significant improvements in kNN Classification.
An Introduction to Variational Methods for Graphical Models
- Michael I. Jordan, Zoubin Ghahramani, T. Jaakkola, L. Saul
- Computer ScienceMachine 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), and describes a general framework for generating variational transformations based on convex duality.
Think Globally, Fit Locally: Unsupervised Learning of Low Dimensional Manifold
Locally linear embedding (LLE), an unsupervised learning algorithm that computes low dimensional, neighborhood preserving embeddings of high dimensional data, is described and several extensions that enhance its performance are discussed.
Kernel Methods for Deep Learning
A new family of positive-definite kernel functions that mimic the computation in large, multilayer neural nets are introduced that can be used in shallow architectures, such as support vector machines (SVMs), or in deep kernel-based architectures that the authors call multilayers kernel machines (MKMs).
Beyond blacklists: learning to detect malicious web sites from suspicious URLs
This paper describes an approach to this problem based on automated URL classification, using statistical methods to discover the tell-tale lexical and host-based properties of malicious Web site URLs.
Learning a kernel matrix for nonlinear dimensionality reduction
This work investigates how to learn a kernel matrix for high dimensional data that lies on or near a low dimensional manifold and shows how to discover a mapping that "unfolds" the underlying manifold from which the data was sampled.
Identifying suspicious URLs: an application of large-scale online learning
It is demonstrated that recently-developed online algorithms can be as accurate as batch techniques, achieving classification accuracies up to 99% over a balanced data set.
Fast solvers and efficient implementations for distance metric learning
A highly efficient solver for the particular instance of semidefinite programming that arises in LMNN classification is described; this solver can handle problems with billions of large margin constraints in a few hours.
Unsupervised Learning of Image Manifolds by Semidefinite Programming
An algorithm for unsupervised learning of image manifolds by semidefinite programming that computes a low dimensional representation of each image with the property that distances between nearby images are preserved.