Mutual Information between Discrete and Continuous Data Sets
نویسندگان
چکیده
منابع مشابه
Mutual Information between Discrete and Continuous Data Sets
Mutual information (MI) is a powerful method for detecting relationships between data sets. There are accurate methods for estimating MI that avoid problems with "binning" when both data sets are discrete or when both data sets are continuous. We present an accurate, non-binning MI estimator for the case of one discrete data set and one continuous data set. This case applies when measuring, for...
متن کاملEstimating Mutual Information for Discrete-Continuous Mixtures
Estimating mutual information from observed samples is a basic primitive, useful in several machine learning tasks including correlation mining, information bottleneck clustering, learning a Chow-Liu tree, and conditional independence testing in (causal) graphical models. While mutual information is a well-defined quantity in general probability spaces, existing estimators can only handle two s...
متن کاملMeasuring relevance between discrete and continuous features based on neighborhood mutual information
Measures of relevance between features play an important role in classification and regression analysis. Mutual information has been proved an effective measure for decision tree construction and feature selection. However, there is a limitation in computing relevance between numerical features with mutual information due to problems of estimating probability density functions in high-dimension...
متن کاملData-Driven Estimation Of Mutual Information Between Dependent Data
“To be considered for the 2017 IEEE Jack Keil Wolf ISIT Student Paper Award.” We consider the problem of estimating mutual information between dependent data, an important problem in many science and engineering applications. We propose a data-driven, non-parametric estimator of mutual information in this paper. The main novelty of our solution lies in transforming the data to frequency domain ...
متن کاملHyperspectral Data Selection from Mutual Information Between Image Bands
This work presents a band selection method for multi and hyperspectral images using correlation among bands based on mutual information measures. The relationship among bands are represented by means of the transinformation matrix. A process based on a Deterministic Annealing optimization is applied to the transinformation matrix in order to obtain a reduction of this matrix looking for the ima...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: PLoS ONE
سال: 2014
ISSN: 1932-6203
DOI: 10.1371/journal.pone.0087357