L
Lawrence K. Saul
Researcher at University of California, San Diego
Publications - 138
Citations - 40154
Lawrence K. Saul is an academic researcher from University of California, San Diego. The author has contributed to research in topics: Hidden Markov model & Nonlinear dimensionality reduction. The author has an hindex of 49, co-authored 133 publications receiving 37255 citations. Previous affiliations of Lawrence K. Saul include Massachusetts Institute of Technology & University of Pennsylvania.
Papers
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Journal ArticleDOI
Mixed Memory Markov Models: Decomposing Complex Stochastic Processes as Mixtures of Simpler Ones
TL;DR: A set of generalized Baum-Welch updates for factorial hidden Markov models that make use of the transition matrices of these models as a convex combination—or mixture—of simpler dynamical models are derived.
Proceedings Article
A Generalized Linear Model for Principal Component Analysis of Binary Data
TL;DR: An alternating least squares method is derived to estimate the basis vectors and generalized linear coefficients of the logistic PCA model, a generalized linear model for dimensionality reduction of binary data that is related to principal component analysis (PCA) and is much better suited to modeling binary data than conventional PCA.
Proceedings Article
Multiplicative Updates for Nonnegative Quadratic Programming in Support Vector Machines
TL;DR: The asymptotic convergence of the updates is analyzed and it is shown that the coefficients of non-support vectors decay geometrically to zero at a rate that depends on their margins.
Proceedings ArticleDOI
Large Margin Gaussian Mixture Modeling for Phonetic Classification and Recognition
Fei Sha,Lawrence K. Saul +1 more
TL;DR: A framework for large margin classification by Gaussian mixture models (GMMs), which have many parallels to support vector machines (SVMs) but use ellipsoids to model classes instead of half-spaces is developed.
Proceedings ArticleDOI
Modeling distances in large-scale networks by matrix factorization
Yun Mao,Lawrence K. Saul +1 more
TL;DR: In this paper, the authors propose a model for representing and predicting distances in large-scale networks by matrix factorization, which is useful for network distance sensitive applications, such as content distribution networks, topology-aware overlays, and server selections.