Open AccessProceedings Article
Map-Reduce for Machine Learning on Multicore
Cheng-Tao Chu,Sang K. Kim,Yi-an Lin,Yuanyuan Yu,Gary Bradski,Kunle Olukotun,Andrew Y. Ng +6 more
- Vol. 19, pp 281-288
TLDR
This work shows that algorithms that fit the Statistical Query model can be written in a certain "summation form," which allows them to be easily parallelized on multicore computers and shows basically linear speedup with an increasing number of processors.Abstract:
We are at the beginning of the multicore era. Computers will have increasingly many cores (processors), but there is still no good programming framework for these architectures, and thus no simple and unified way for machine learning to take advantage of the potential speed up. In this paper, we develop a broadly applicable parallel programming method, one that is easily applied to many different learning algorithms. Our work is in distinct contrast to the tradition in machine learning of designing (often ingenious) ways to speed up a single algorithm at a time. Specifically, we show that algorithms that fit the Statistical Query model [15] can be written in a certain "summation form," which allows them to be easily parallelized on multicore computers. We adapt Google's map-reduce [7] paradigm to demonstrate this parallel speed up technique on a variety of learning algorithms including locally weighted linear regression (LWLR), k-means, logistic regression (LR), naive Bayes (NB), SVM, ICA, PCA, gaussian discriminant analysis (GDA), EM, and backpropagation (NN). Our experimental results show basically linear speedup with an increasing number of processors.read more
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NOMAD: Non-locking, stOchastic Multi-machine algorithm for Asynchronous and Decentralized matrix completion
TL;DR: In this article, a non-locking, stOchastic multi-machine algorithm for asynchronous and decentralized matrix completion (NOMAD) is proposed. But it is not a lock-free parallel algorithm.
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Evolutionary Feature Selection for Big Data Classification: A MapReduce Approach
Daniel Peralta,Sara del Río,Sergio Ramírez-Gallego,Isaac Triguero,José Manuel Benítez,Francisco Herrera +5 more
TL;DR: A feature selection algorithm based on evolutionary computation that uses the MapReduce paradigm to obtain subsets of features from big datasets, improving both the classification accuracy and its runtime when dealing with big data problems.
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TL;DR: This work describes the methodology that won the ECBDL'14 big data challenge for a bioinformatics big data problem, named as ROSEFW-RF, which is based on several MapReduce approaches to balance the classes distribution through random oversampling and detect the most relevant features via an evolutionary feature weighting process.
Proceedings ArticleDOI
On scheduling in map-reduce and flow-shops
TL;DR: This work formalizes job scheduling in map-reduce as a novel generalization of the two-stage classical flexible flow shop (FFS) problem: instead of a single task at each stage, a job now consists of a set of tasks per stage.
Journal ArticleDOI
On Distributed Fuzzy Decision Trees for Big Data
TL;DR: A distributed FDT learning scheme shaped according to the MapReduce programming model for generating both binary and multiway FDTs from big data, which relies on a novel distributed fuzzy discretizer that generates a strong fuzzy partition for each continuous attribute based on fuzzy information entropy.
References
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MapReduce: simplified data processing on large clusters
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TL;DR: This paper presents the implementation of MapReduce, a programming model and an associated implementation for processing and generating large data sets that runs on a large cluster of commodity machines and is highly scalable.
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TL;DR: Principal Component Analysis is a multivariate exploratory analysis method useful to separate systematic variation from noise and to define a space of reduced dimensions that preserve noise.