Expectation-Maximization Tensor Factorization for Practical Location Privacy Attacks

A Practical Method for Self-adapting Gaussian Expectation Maximization

Split-and-merge techniques have been demonstrated to be effective in overtaking the convergence problems in classical EM. In this paper we follow a split-and-merge approach and we propose a new EM algorithm that makes use of a on-line variable number of mixture Gaussians components. We introduce a measure of the similarities to decide when to merge components. A set of adaptive thresholds keeps...

Expectation Maximization

Expectation Maximization

The Expectation Maximization (EM) algorithm [1, 2] is one of the most widely used algorithms in statistics. Suppose we are given some observed data X and a model family parametrized by θ, and would like to find the θ which maximizes p(X |θ), i.e. the maximum likelihood estimator. The basic idea of EM is actually quite simple: when direct maximization of p(X |θ) is complicated we can augment the...

Location Privacy against Traffic Analysis Attacks

We propose a method which provides location privacy against traffic analysis attacks. Traffic analysis is the process of intercepting and examining messages in order to deduce information from patterns in communication. It can be described as the passive attacks that try to deduce the traffic pattern based on the eavesdropped information. They launch an active attack according to the achieved i...

Week 9: Expectation Maximization

Last week, we saw how we could represent clustering with a probabilistic model. In this model, called a Gaussian mixture model, we model each datapoint x i as originating from some cluster, with a corresponding cluster label y i distributed according to p(y), and the corresponding distribution for that cluster given by a multivariate Gaussian: p(x|y = k) =