Hub Minerhttp://mloss.orgUpdates and additions to Hub MinerenThu, 22 Jan 2015 16:33:51 -0000Hub Miner 1.1<html><p>Hub Miner is an OpenML-compatible machine learning library with an emphasis on instance-based high-dimensional data analysis under the asymmetric distribution of relevance, that is referred to as hubness. </p> <p>Some updates to the previous version: - BibTex support for all algorithm implementations, making all of them easy to reference (via algref package). </p> <ul> <li><p>Two more hubness-aware approaches (meta-metric-learning and feature construction) </p> </li> <li><p>An implementation of Hit-Miss networks for analysis. </p> </li> <li><p>Several minor bug fixes. </p> </li> <li><p>The following instance selection methods were added: HMScore, Carving, Iterative Case Filtering, ENRBF. </p> </li> <li><p>The following clustering quality indexes were added: Folkes-Mallows, Calinski-Harabasz, PBM, G+, Tau, Point-Biserial, Hubert's statistic, McClain-Rao, C-root-k. </p> </li> <li><p>Some more experimental scripts have been included. </p> </li> <li><p>Extensions in the estimation of hubness risk. </p> </li> <li><p>Alias and weighted reservoir methods for weight-proportional random selection. </p> </li> </ul> <p>OpenML compatibility assures that the classification experiments can be performed in a networked manner and the results can be uploaded to OpenML servers for cross-comparisons with other systems and implementations. This same functionality has recently been added to Weka, R and RapidMiner. </p> <p>The library implements a series of recently proposed hubness-aware methods for learning in many dimensions. </p> <p>Hub Miner implements custom methods for classification, clustering, metric learning, instance selection, outlier detection and other common machine learning tasks, as well as a set of baselines and a powerful experimental framework that includes testing under various challenging conditions. </p> <p>It is implemented entirely in Java, so it should be easily portable to all platforms. </p> <p>Hub Miner also contains Image Hub Explorer, a tool for experimentation with quantized image feature representations that has been presented at the European Conference on Machine Learning in 2013. </p> <p>Hub Miner contains all the methods that I have proposed in my Ph.D. thesis while studying at the Artificial Intelligence Laboratory, but it actually contains a lot more than that. </p> <p>There is a detailed user manual at the GitHub repo, so all new users will be able to find their way in the library with relative ease. </p> <p>This is an announcement of the first release. </p> <p>Future releases are expected to contain more standard baselines and more hubness-aware methods, as well as better support for handling large-scale datasets. </p> <p>For more details, You are also invited to visit the Hub Miner page on my homepage: </p> <p>Feel free to contact me with comments, suggestions and ideas, as well as queries on how to use the software if You are interested. </p></html>Nenad TomasevThu, 22 Jan 2015 16:33:51 -0000 learningdata miningoutlier detectionmetric learningdata reductionhigh dimensional data analysishubness