RankMap: A Framework for Distributed Learning From Dense Data Sets

RankMap: A Platform-Aware Framework for Distributed Learning from Dense Datasets

This paper introduces RankMap, a platform-aware end-to-end framework for efficient execution of a broad class of iterative learning algorithms for massive and dense datasets. Our framework exploits data structure to scalably factorize it into an ensemble of lower rank subspaces. The factorization creates sparse low-dimensional representations of the data, a property which is leveraged to devise...

Distributed Learning on Very Large Data Sets

One approach to learning from intractably large data sets is to utilize all the training data by learning models on tractably sized subsets of the data. The subsets of data may be disjoint or partially overlapping. The individual learned models may be combined into a single model or a voting approach may be used to combine the classi cations of a set of models. An approach to learning models in...

A New Framework for Distributed Multivariate Feature Selection

Feature selection is considered as an important issue in classification domain. Selecting a good feature through maximum relevance criterion to class label and minimum redundancy among features affect improving the classification accuracy. However, most current feature selection algorithms just work with the centralized methods. In this paper, we suggest a distributed version of the mRMR featu...

Ensembles of data-reduction-based classifiers for distributed learning from very large data sets

An ensemble of classifiers is a set of classification models and a method to combine their predictions into a joint decision. They were primarily devised to improve classification accuracies over individual classifiers. However, the growing need for learning from very large data sets has opened new application areas for this strategy. According to this approach, new ensembles of classifiers hav...

A Mixture-of-Experts Framework for Learning from Imbalan ed Data Sets