We then propose an alternative information theoretic measure of anonymity which takes into account the probabilities of users sending and receiving the messages and show how to calculate it for a message in a standard mixbased anonymity system. It is particularly useful in large scale applications like searching for an image. We describe the distance metric learning problem and analyze its main mathematical foundations. Add a list of references from and to record detail pages load references from and. Information theory and machine learning xiaokai wei university of illinois at chicago email. We formulate the metric learning problem as that of minimizing the differential relative entropy between two multivariate gaussians under constraints on the mahalanobis distance function. Person reidentification using kernelbased metric learning methods. A beginners guide to the metric system the new yorker. Informationtheoretic metric learning jason davis, brian kulis, prateek jain, suvrit sra, inderjit dhillon abstract. Robust recognition via information theoretic learning. In particular, the correntropy as a nonlinear similarity measure in kernel space has its root in renyis entropy. Informationtheoretic metric learning proceedings of the 24th. A novel adaptive algorithm for diffusion networks using.
Informationtheoretic metric learning microsoft research. Informationtheoretic metric learning linkedin slideshare. In john lloyd, veronica dahl, ulrich furbach, manfred kerber, kungkiu lau, catuscia palamidessi, luis moniz pereira, yehoshua sagiv, and peter j. My group performs fundamental and applied research in machine learning, artificial intelligence, and robotics with a focus on developing theory and systems that tightly integrate perception. A novel adaptive algorithm for diffusion networks using projections onto hyperslabs. Citeseerx an informationtheoretic definition of similarity. Metric learning involves finding a suitable metric for a given set of datapoints with sideinformation. Itml is a matlab implementation of information theoretic metric learning algorithm. Nonlinear dynamic neural network for textindependent speaker. An information theoretic analysis of hard and soft assignment methods for clustering.
We formulate the problem as that of minimizing the differential. Deep metric learning has been extensively explored recently, which trains a deep neural network to produce discriminative embedding features. The oxford handbook of social networks, oxford press, 2020. In this paper, we present an information theoretic approach to learning a mahalanobis distance function.
In this paper, we present an informationtheoretic approach to learning a mahalanobis distance function. How is information theoretic metric learning algorithm abbreviated. Most existing methods usually enforce the model to be indiscriminating to intraclass variance, which makes the model overfitting to the training set to minimize loss functions on these specific changes. In 2010 2nd international workshop on cognitive information processing, cip2010 pp. The blue social bookmark and publication sharing system. Pdf informationtheoretic semisupervised metric learning. Information theory studies the quantification, storage, and communication of information. Unlike all other previous texts, this book dives deep into the theory first, looking at foundational and hard questions, before moving on to specific algorithms. Should you wish to have your publications listed here, you can either email us your bibtex.
Informationtheoretic metric learning proceedings of the. Pdf abstract bibtex this dissertation demonstrates, in a nonsemantic informationtheoretic framework, how the principles of maximisation of relevant information and information parsimony can guide. By convention, we use lowercase symbols to denote local informationtheoretic measures. Via a surprising equivalence, we show that this problem can be solved as a lowrank kernel learning problem. We present here a definition of system complexity and a quantitative metric for measuring that complexity based on information theory. Similarity is an important and widely used concept. System complexity is considered a key driver of the inability of current system design practices to at times not recognize performance, cost, and schedule risks as they emerge. The shannon information content of a given symbol x is the codelength for that symbol in an. Davis and brian kulis and prateek jain and suvrit sra and inderjit s. In yannis labrou and tim finin, editors, proceedings of the cikm workshop on intelligent information agents, third international conference on information and knowledge management cikm 94, gaithersburg, md, december, 1994.
It uses a decision tree as a predictive model to go from observations about an item represented in the branches to conclusions about the items target value represented in the leaves. Large scale online learning of image similarity through. Our metric learning problem is formulated as a quadratic semidefinite. Classspecific mahalanobis distance metric learning for biological. Itl uses descriptors from information theory entropy and. Nov 02, 2017 we introduce a new metric, jointpolicy correlation, to quantify this effect. Bibtex will select only the quoted references, and arrange them alphabetically if the style is such. Informationtheoretic metric learning jason davis, brian kulis, suvrit sra and inderjit dhillon dept. We introduce a new metric, jointpolicy correlation, to quantify this effect. Find, read and cite all the research you need on researchgate. Certification of compiler optimizations using kleene algebra with tests. Integration of representation into goaldriven behaviourbased robots.
Allen school of computer science and engineering cse at the university of washington where i direct the uw robot learning laboratory. Davis, brian kulis, suvrit sra, and inderjit dhillon the university of texas at austin december 9, 2006 presenter. Based on measure theory, we present a change of measure to build a finite variance density allowing the use of resampling estimators. This is a list of publications, aimed at being a comprehensive bibliography of the field. Distance metric learning dml is an emerging field of machine learning. Distance metric has an important role in many machine learning algorithms.
Since correntropy especially with a small kernel bandwidth. Machine learning, proceedings of the twentyfourth international conference icml 2007, corvallis, oregon, usa june 2007. We consider bayesian estimation of informationtheoretic quantities from data, using a dirichlet prior. We formulate the problem as that of minimizing the differential relative entropy between two multivariate gaussians under constraints on the distance function. Obermayer, editors, advances in neural information processing systems 15, pages 521528, cambridge, ma, 2003. We motivate and present a definition of an embodied, grounded individual sensorimotor interaction history, which captures the timeextended behavior characteristics of humans and many animals. Dec 19, 2012 during the last two decades, concentration inequalities have been the subject of exciting developments in various areas, including convex geometry, functional analysis, statistical physics, highdimensional statistics, pure and applied probability theory, information theory, theoretical computer science, and learning theory. Understanding machine learning is a most welcome breath of fresh air into the libraries of machine learning enthusiasts and students.
We motivate and present a definition of an embodied, grounded individual sensorimotor interaction history, which captures the timeextended behavior characteristics of humans and. Add open access links from to the list of external document links if available. Dhillon, title informationtheoretic metric learning, booktitle in nips 2006 workshop on learning to compare examples, year 2007. An informationtheoretic perspective on model interpretation jianbo chen1 2 le song3 4 martin j. We describe an algorithm for general marl, based on approximate best responses to mixtures of. Classification tree analysis is when the predicted outcome is the class discrete to which the data belongs. In this paper we look closely at the popular metric of anonymity, the anonymity set, and point out a number of problems associated with it. Dtwglobal constraint learning using tabu search algorithm. Acknowledging the uncertainty of the event space size m and the dirichlet priors. Informationtheoretic private information retrieval. Decision trees used in data mining are of two main types. Part of the lecture notes in computer science book series lncs, volume 7325. Towards an information theoretic metric for anonymity. This allows to use all the scikit learn routines for pipelining, model selection, etc with metric learning algorithms through a unified interface.
Grounded sensorimotor interaction histories in an information. Learning a measure of similarity between pairs of objects is an important generic problem in machine learning. Apibased and informationtheoretic metrics for measuring the quality of software modularization s. Davis, brian kulis, prateek jain, suvrit sra, inderjit s. Bibtex entries for the publications of joao pedro hespanha. However, the information theoretic derived metric of the nonparametric parzens density estimator displays infinite variance, limiting the direct use of resampling estimators.
In this paper we present a novel design for a nonlinear dynamic neural network to implement textindependent speaker recognition without the benefit of exact voice signatures. The dynamic properties between the input neuron and the output neuron make use of a nonlinear highorder synaptic neural model with memory of previous input signals. Informationtheoretic metric learning ut computer science. Computational social science, machine learning, social networks. Itml information theoretic metric learning algorithm. When using this code, please cite itml and the relevant paper. Informationtheoretic metric learning center for big. Proceedings of the 24th international conference on machine learning. Concentration of measure inequalities in information theory.
The landmark event that established the discipline of information theory and brought it to immediate worldwide attention was the publication of claude e. A decisiontheoretic generalization of online learning. An informationtheoretic view on language complexity and. Kernelized informationtheoretic metric learning for cancer diagnosis using highdimensional molecular profiling data, acm transactions on knowledge discovery from data journal. Distance metric learning dml is an effective similarity learning tool to learn a distance function from examples to enhance the model performance in appl. Machine learning and knowledge discovery in databases pp 1256 cite as. An informationtheoretic metric of system complexity with application to engineering system design douglas allaire, chelsea he, john deyst, and karen willcox department of aeronautics. Itml stands for information theoretic metric learning algorithm. As part of scikit learn contrib, the api of metric learn is compatible with scikit learn, the leading library for machine learning in python. This monograph focuses on some of the key modern mathematical tools. In which we try to give a basic intuitive sense of what reinforcement learning is and how it differs and relates to. Home page for professor michael kearns, university of.
Itml is defined as information theoretic metric learning algorithm. Robust structural metric learning suppresses noisy features. Information theoretic learning itl was initiated in the late 90s at cnel and has been a center piece of the research effort. Informationtheoretic models for social interaction, phd thesis, university of hertfordshire, hatfield, 2012. Iclr is globally renowned for presenting and publishing cuttingedge research on all aspects of deep learning used in the fields of artificial intelligence, statistics and data science, as well as important application areas such as machine vision, computational biology, speech recognition, text understanding, gaming, and robotics. The particular subjects covered include motion planning, discrete planning, planning under uncertainty, sensorbased planning, visibility, decision theoretic planning, game theory, information spaces, reinforcement learning, nonlinear systems, trajectory planning, nonholonomic planning, and kinodynamic planning. Information theoretic metric learning itml sparse determinant metric learning sdml least squares metric learning lsml neighborhood components analysis nca. Ok, this is is an information theoretic argument rather than a lower bound on a computational problem, but the idea is the same. Related work supervised metric learning is a wellstudied problem, of which some representative methods are information. Distance metric learning with application to clustering with side information. The dynamic neural network is realized in the short. The authors resort to a new information theoretic concept, correntropy, as a robust measure and apply it to solve robust face recognition and object recognition problems. Learning term dependency links using information theoretic inclusion measure, the 2007 icdm workshop on mining graphs and complex structures mgcs2007, omaha, ne, usa, pp.
A game theoretic approach to explain the output of any machine learning model. Citeseerx semisupervised metric learning using pairwise. Part of the lecture notes in computer science book series lncs, volume. While the metric learning approaches are moved to the transformation space with distance information, the method is basically based on a w projection matrix. Learning from history for behaviorbased mobile robots in nonstationary. The information theoretic learning also links information theory, nonparametric estimators, and reproducing kernel hilbert spaces rkhs in a simple and unconventional way. We formulate the metric learning problem as that of minimizing the differential relative entropy between two multivariate gaussians under constraints on the mahalanobis. Dhillon, title informationtheoretic metric learning, booktitle icml, year 2007. Information theoretic metric learning itml sparse determinant metric learning sdml least squares metric learning lsml neighborhood components analysis nca local fisher discriminant analysis lfda relative components analysis rca metric learning for kernel regression mlkr mahalanobis metric for clustering mmc dependencies. Previous definitions of similarity are tied to a particular application or a form of knowledge representation. Informationtheoretic metric learning videolectures. Computational logic cl2000, volume 1861 of lecture notes in artificial intelligence, pages 568.
Intrinsic motivation for reinforcement learning systems. An informationtheoretic metric of system complexity with. Gesture modeling by hankletbased hidden markov model. Citeseerx document details isaac councill, lee giles, pradeep teregowda. Decision tree learning is one of the predictive modelling approaches used in statistics, data mining and machine learning. Enter your mobile number or email address below and well send you a link to download the free kindle app. Proceedings of the thirteenth conference on uncertainty in artificial intelligence, pp.
Bruno abrahao research assistant professor new york. Distributed informationtheoretic metric learning in apache spark. Informationtheoretic analysis of stability and bias of. We describe an algorithm for general marl, based on approximate best responses to mixtures of policies generated using deep reinforcement learning, and empirical game theoretic analysis to compute metastrategies for policy selection. Bibsonomy helps you to manage your publications and bookmarks, to collaborate with your colleagues and to find new interesting material for your research. Contribute to junjiehuitml development by creating an account on github.
Correntopy induced metric cim behaves as the l2norm for small distances and progressively approaches the l1norm and then converges to l0 at infinity. Bibliographic details on information theoretic metric learning. Informationtheoretic semisupervised metric learning via. It was originally proposed by claude shannon in 1948 to find fundamental limits on signal processing and communication operations such as data compression, in a landmark paper titled a mathematical theory of communication. I introduction one of the fundamental problems of our technology driven society is the huge amounts of data that are. Notice that example 2 fails for trees, which do generically. Informationtheoretic semisupervised metric learning via entr opy regularization on unlabeled data, which can achieve the sparsity of the posterior distribution grac.
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