Information Theoretical Estimators Toolbox
Authors: Zoltán Szabó
JMLR 2014 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present ITE (information theoretical estimators) a free and open source, multi-platform, Matlab/Octave toolbox that is capable of estimating many different variants of entropy, mutual information, divergence, association measures, cross quantities, and kernels on distributions... ITE also includes a prototype application in a central problem class of signal processing, independent subspace analysis and its extensions. ... Quick Tests: Beyond IPA, ITE provides quick tests to study the efficiency of the estimators. These tests cover (i) analytical value vs. estimation, (ii) positive semi-definiteness of Gram matrices defined by distribution kernels and (iii) image registration problems. |
| Researcher Affiliation | Academia | Zoltán Szabó EMAIL Gatsby Computational Neuroscience Unit Centre for Computational Statistics and Machine Learning University College London Alexandra House, 17 Queen Square, London WC1N 3AR |
| Pseudocode | Yes | >Y1 = rand(3,1000); Y2 = rand(3,2000); %data of interest >mult = 1; %multiplicative constant is important >co = D_initialization( Jdistance ,mult);%initialize the estimator >D = D_estimation(Y1,Y2,co); %estimation Next, we demonstrate how one can construct meta estimators in ITE. We consider the definitions of the initialization and the estimation of the J-distance. The KLdivergence, which is symmetrised in J-distance, is estimated based on the existing k-nearest neighbor technique. function [co] = DJdistance_initialization(mult) co.name = Jdistance ; %name of the estimator co.mult = mult; %importance of multiplicative const. co.member_name = KL_k NN_k ; %method used for KL estimation co.member_co = D_initialization(co.member_name,mult); %initialization function [D_J] = DJdistance_estimation(X,Y,co) D_J = D_estimation(X,Y,co.member_co) + D_estimation(Y,X,co.member_co); |
| Open Source Code | Yes | We present ITE (information theoretical estimators) a free and open source, multi-platform, Matlab/Octave toolbox... ITE is released under the free and open source GNU GPLv3 (...) license. ... The ITE package is available at https://bitbucket.org/szzoli/ite/. |
| Open Datasets | No | The paper describes a software toolbox and its capabilities. While it mentions quick tests and a prototype application, it does not specify any publicly available datasets used for these, nor does it provide links, DOIs, or formal citations for any data. |
| Dataset Splits | No | The paper does not describe the use of any specific datasets, other than randomly generated data for demonstration purposes, and therefore does not provide any dataset split information. |
| Hardware Specification | No | The paper mentions that 'a single general desktop computer can handle about a million observations' but does not provide any specific details about the hardware, such as CPU, GPU models, or memory specifications. |
| Software Dependencies | No | The paper states that the ITE package 'only needs a Matlab or an Octave environment with standard toolboxes' and suggests 'Visual C++ (GCC) compiler' on Windows (Linux), but it does not provide specific version numbers for any of these software dependencies. |
| Experiment Setup | No | The paper provides code examples to demonstrate the use of the toolbox but does not specify any concrete hyperparameter values, training configurations, or system-level settings for experimental reproduction. |