1,721,096 research outputs found
Stochastic Modeling for Efficient Computation of Information Theoretic Quantities
No full textReplace with the thesis abstract Thesis Supervisor: Naftali Tishby Title: Professor, Hebrew University of Jerusalem Thesis Supervisor: Ran El-Yaniv Title: Doctor, Technion Contents
What Is a Macrostate? Subjective Observations and Objective Dynamics
Full text linkWe consider the question of whether thermodynamic macrostates are objective consequences of dynamics, or subjective reflections of our ignorance of a physical system. We argue that they are both; more specifically, that the set of macrostates forms the unique maximal partition of phase space which 1) is consistent with our observations (a subjective fact about our ability to observe the system) and 2) obeys a Markov process (an objective fact about the system's dynamics). We review the ideas of computational mechanics, an information-theoretic method for finding optimal causal models of stochastic processes, and argue that macrostates coincide with the ``causal states'' of computational mechanics. Defining a set of macrostates thus consists of an inductive process where we start with a given set of observables, and then refine our partition of phase space until we reach a set of states which predict their own future, i.e. which are Markovian. Macrostates arrived at in this way are provably optimal statistical predictors of the future values of our observables
Detecting Anomalies in People’s Trajectories using Spectral Graph Analysis
No full textVideo surveillance is becoming the technology of choice for monitoring crowded areas for security threats. While video provides ample information for human inspectors, there is a great need for robust automated techniques that can efficiently detect anomalous behavior in streaming video from single or multiple cameras. In this work we synergistically combine two state-of-the-art methodologies. The first is the ability to track and label single person trajectories in a crowded area using multiple video cameras, and the second is a new class of novelty detection algorithms based on spectral analysis of graphs. By representing the trajectories as sequences of transitions between nodes in a graph, shared individual trajectories capture only a small subspace of the possible trajectories on the graph. This subspace is characterized
by large connected components of the graph, which are spanned by the eigenvectors with the low eigenvalues of the graph Laplacian matrix. Using this technique, we develop robust invariant distance measures for detecting anomalous trajectories, and demonstrate their application on real video data
Theory and Application of the Information Bottleneck Method
No full textIn 1999, Naftali Tishby et al [...
Theory and application of the information bottleneck method
No full textIn 1999, Naftali Tishby et al [...
Course 16 The emergence of relevant data representations: An information theoretic approach
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