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. Similarity is an important and widely used concept. Informationtheoretic semisupervised metric learning via entropy. However, the information theoretic derived metric of the nonparametric parzens density estimator displays infinite variance, limiting the direct use of resampling estimators. Metric learning on expression data for gene function prediction.
We first use the structure of the ontology to probabilistically model, via a bayesian network, the prior distribution of protein experimental annotation. If you continue browsing the site, you agree to the use of cookies on this website. Informationtheoretic metric learning jason davis, brian kulis, prateek jain, suvrit sra, inderjit dhillon abstract. Machine learning seminar distance metric learning lmnn, lmca by sanghyuk chun slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. We formulate an entropylike functional on positive definite matrices based on renyis axiomatic definition of entropy and examine some key properties of this functional that lead to the concept of infinite divisibility. An information theoretic perspective on model interpretation %a jianbo chen %a le song %a martin wainwright %a michael jordan %b proceedings of the 35th international conference on machine learning %c proceedings of machine learning research %d 2018 %e jennifer dy %e andreas krause %f. Pdf by claudia diaz, carmela troncoso, and george danezis. The proposed formulation avoids the plug in estimation of. An information theoretic framework for multiview learning karthik sridharan and sham m. Itml characterizes the metric using a mahalanobis distance function and learns the associated parameters using. We propose a general information theoretic approach to semisupervised metric learning called seraph semisupervised metric learning paradigm with hypersparsity that does not rely upon the manifold assumption. Many metric learning algorithms have been proposed, most of them focus just on single view circumstances, and only a few deal with multiview data.
Two fundamental problems are critical for person reidentification, feature representation and metric learning. Download bibtex in this paper, we present an information theoretic approach to learning a mahalanobis distance function. Informationtheoretic metric learning proceedings of the 24th. Davis and brian kulis and prateek jain and suvrit sra and inderjit s. Given the probability parameterized by a mahalanobis distance, we maximize its entropy on labeled data and. Metric learning is an important problem in machine learning and pattern recognition. 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.
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. 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. Sep 28, 2019 metric learning has also been applied to afp combined with multipleinstance learning xu et al. We present an informationtheoretic definition of similarity that is applicable as long as there is a probabilistic model. Nonlinear dynamic neural network for textindependent speaker. Distance metric learning for large margin nearest neighbor classification.
The goal of metric learning is to nd a new metric under which \similar data are close and \dissimilar data are far apart xing et al. Informationtheoretic metric learning proceedings of the. We find the projection as a solution to the information theoretic optimization problem of maximizing the information between the projected data and the class labels. Information theoretic learning itl was initiated in the late 90s at cnel and has been a center piece of the research effort. Probabilistic and informationtheoretic approaches to anonymity cached. Allocate resources to learning more about the target velocity and fuel level.
Person reidentification is an important technique towards automatic search of a persons presence in a surveillance video. In this paper, motivated by the cotraining framework, we propose an algorithmindependent framework, named co metric, to learn mahalanobis metrics in multiview settings. Grounded sensorimotor interaction histories in an information. Informationtheoretic model comparison unifies saliency. The method is applied to two datasets using different types of neural. In this paper, we propose a framework for metric learning based on information geometry. Should you wish to have your publications listed here, you can either email us your bibtex. Shannons classic paper a mathematical theory of communication in the bell system technical journal in july and october 1948 prior to this paper, limited informationtheoretic ideas had been developed at bell labs, all implicitly assuming. Information theoretic metric learning 1 metric learning.
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. 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. We find the projection as a solution to the informationtheoretic optimization problem of maximizing the information between the projected data and the class labels. Tracking individuals across camera networks with no overlapping fields is still a challenging problem. We compare the performance of these methods from various angles by adapting the baseline model to four adaptation domains. In order to deal with this problem, in this paper we present an online logdet divergence based metric learning ldml model by making use of the powerfulness of metric learning.
Mar 31, 2020 metric learn contains efficient python implementations of several popular supervised and weaklysupervised metric learning algorithms. In information theoretic learning itl, the measures from information theory entropy, mutual information, divergences, etc. Informationtheoretic model comparison unifies saliency metrics. Nonlinear dynamic neural network for textindependent. An informationtheoretic view of generalization via wasserstein distance. In this paper, we address the nonlinear metric leaning problem with applying in the document classification. Learning a proper distance metric is an important problem in document classification, because the similarities of samples in many problems are usually measured by distance metric.
Informationtheoretic semisupervised metric learning 40. 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. We firstly generate a mahalanobis matrix via learning the training data with ldml model. Towards an information theoretic metric for anonymity. Informationtheoretic semisupervised metric learning via. Information theoretic metric learning jason davis, brian kulis, prateek jain, suvrit sra, inderjit dhillon abstract. As part of scikitlearncontrib, the api of metric learn is compatible with scikitlearn, the leading library for machine learning in python. This paper presents an empirical study on four techniques of language model adaptation, including a maximum a posteriori map method and three discriminative training models, in the application of japanese kanakanji conversion. Publications navy center for applied research in artificial. Central to this is an information theoretic metric space of sensorimotor experience, which is dynamically constructed and reconstructed as the robot acts. An informationtheory based distance metric learning algorithm. Abstractmachine learning algorithms can be viewed as stochastic transformations that map training data to hypotheses. Information theoretic metric learning itml pydml 0. We present some general results determining minimax bounds on statistical risk for density estimation based on certain information theoretic considerations.
The projection, and corresponding metric, should preserve classrelevant information pertaining to different behavior or stimuli. Inferencedriven metric learning 20, 21, informationtheoretic metric learning 22, graph edge sharpening 23, spectral kernel learning 24. The dynamic neural network is realized in the short. Following bousquet and elisseeff, we say that such an algorithm. Informationtheoretic metric learning itml 3, which is also built with positive negative pairs, models the problem with logdeterminant. See this help page for instructions on obtaining such a link. In this paper, we develop a framework for information theoretic learning based on infinitely divisible matrices. An information geometry approach for distance metric learning. Specifically, we minimize the burg divergence of a lowrank kernel to an input kernel, subject to. In which we try to give a basic intuitive sense of what reinforcement learning is and how it differs and relates to other fields, e. Given the probability parameterized by a mahalanobis distance, we maximize the entropy of that probability on labeled. An informationtheoretic metric of system complexity with. These bounds depend only on metric entropy conditions and are used to identify the minimax rates of convergence. In this article, we develop an information theoretic framework for evaluating the prediction accuracy of computergenerated ontological annotations.
Given an initial metric, it learns the nearest metric that satisfies some similarity and dissimilarity. Kak, in ieee transactions on software engineering, volume 33, 2007. We present here a definition of system complexity and a quantitative metric for measuring that complexity based on information theory. Brockmeier aj, sanchez giraldo lg, emigh ms, bae j, choi js, francis jt, principe jc. Due to the variations of viewpoint, pose, and illumination, a given individual may appear considerably different across different camera views. We propose a general informationtheoretic approach called seraph semisupervised metric learning paradigm with hypersparsity for metric learning that does not rely upon the manifold assumption. We translate the problem of learning an optimal distance metric to that of learning the optimal gaussian with respect to an entropic objective. Contribute to junjiehuitml development by creating an account on github. An information theoretic framework for multiview learning. In this paper, we present an informationtheoretic approach to learning a. Bibtex entry does additional information always reduce anonymity.
Davis, brian kulis, suvrit sra, and inderjit dhillon the university of texas at austin december 9, 2006 presenter. Given the probability parameterized by a mahalanobis distance, we maximize the entropy of that probability on labeled data and minimize it on unlabeled data following entropy regularization, which. Given the probability parameterized by a mahalanobis distance, we maximize the entropy of that probability on labeled data and minimize it on unlabeled data following entropy. Informationtheoretic metric learning jason davis, brian kulis, suvrit sra and inderjit dhillon dept. We propose a general information theoretic approach called seraph semisupervised metric learning paradigm with hypersparsity for metric learning that does not rely upon the manifold assumption. Jun 14, 2017 learning a proper distance metric is an important problem in document classification, because the similarities of samples in many problems are usually measured by distance metric. Decision tree learning is one of the predictive modelling approaches used in statistics, data mining and machine learning. Apibased and informationtheoretic metrics for measuring the quality of software modularization s.
We present a proofofconcept study that sketches the use of compression algorithms to assess kolmogorov complexity, which is a textbased, quantitative, holistic, and global measure of structural. The landmark event that established the discipline of information theory and brought it to immediate worldwide attention was the publication of claude e. Document classification via nonlinear metric learning. Deep learning for person reidentification using support. An informationtheoretic metric of system complexity with application to engineering system design douglas allaire, chelsea he, john deyst, and karen willcox department of aeronautics and astronautics.
Sham kakade 1 metric learning in knearest neighbors knn and other classi. Distributed informationtheoretic metric learning in apache spark. We formulate the problem as that of minimizing the differential relative entropy between two multivariate. Itml is a matlab implementation of information theoretic metric learning algorithm. Discriminative histogram intersection metric learning and its. Based on measure theory, we present a change of measure to build a finite variance density allowing the use of resampling estimators. 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.
A game theoretic approach to explain the output of any machine learning model. Previous definitions of similarity are tied to a particular application or a form of knowledge representation. An empirical study on language model adaptation using a. Informationtheoretic metric learning center for big.
Citeseerx an informationtheoretic definition of similarity. Information theory studies the quantification, storage, and communication of information. We formulate the problem as that of minimizing the differential relative entropy between two multivariate gaussians under constraints on the distance function. Using a principled metric for model comparison information gain, we quantify progress in the field and show how formulating the models. 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. Proceedings of the 24th international conference on machine. An informationtheoretic view of generalization via. Davis, brian kulis, suvrit sra, and inderjit dhillon informationtheoretic metric learning.
Informationtheoretic analysis of stability and bias of. Informationtheoretic semisupervised metric learning 2 1 introduction how to learn a good distance metric for the input data domain is a crucial issue for many distancebased learning algorithms. Person reidentification by local maximal occurrence. Citeseerx document details isaac councill, lee giles, pradeep teregowda. Distance metric learning dml is an effective similarity learning tool to learn a distance function from examples to enhance the model performance in. Informationtheoretic metric learning linkedin slideshare. Dhillon, title informationtheoretic metric learning, booktitle in nips 2006 workshop on learning to compare examples, year 2007. This is a list of publications, aimed at being a comprehensive bibliography of the field. Principe, title information theoretic metric learning. We express this problem as a particular bregman optimization problem. Using a principled metric for model comparison information gain, we quantify progress in the field and show how formulating the models probabilistically. Inferencedriven metric learning 20, 21, information theoretic metric learning 22, graph edge sharpening 23, spectral kernel learning 24. Download bibtex in this paper, we present an informationtheoretic approach to learning a mahalanobis distance function. Via a surprising equivalence, we show that this problem can be solved as a lowrank kernel learning problem.
Informationtheoretic metric learning microsoft research. Metric learning involves finding a suitable metric for a given set of datapoints with sideinformation regarding distances between few datapoints. In this paper, we present an informationtheoretic approach to learning a mahalanobis distance function. Download bibtex %0 conference paper %t learning to explain. Itl uses descriptors from information theory entropy and divergences estimated directly from the data to substitute the conventional statistical descriptors of variance and covariance. We present results of robotic experiments that establish the predictive efficacy of the space and we show the robot developing the capacity to play the simple interaction game peekaboo. Metric learning involves finding a suitable metric for a given set of datapoints with side information regarding distances between few datapoints. While the metric learning approaches are moved to the transformation space with distance information, the method is basically based on a w projection matrix. As part of scikitlearncontrib, the api of metriclearn is compatible with scikitlearn, the leading library for machine learning in python.
Intracortical neural recordings are typically highdimensional due to many electrodes, channels, or units and high sampling rates, making it very difficult to. Informationtheoretic metric learning computer science. For example, in supervised learning, such as regression, the problem can be. Informationtheoretic metric learning connecting repositories. In this paper, we present an information theoretic approach to learning a mahalanobis distance function. Informationtheoretic metric learning center for big data. The key idea is to construct two kernel matrices for the given training data. Jan 16, 20 in this paper, we develop a framework for information theoretic learning based on infinitely divisible matrices. Informationtheoretic evaluation of predicted ontological.
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