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
Citations
More filters
Journal ArticleDOI
On the Feasibility of Distributed Kernel Regression for Big Data
TL;DR: It is shown that, with proper data segmentation, DKR leads to an estimator that is generalization consistent to the unknown regression function, which theoretically justifies DKR and sheds light on more advanced distributive algorithms for processing Big Data.
PARABLE: A PArallel RAndom-partition Based HierarchicaL ClustEring Algorithm for the MapReduce Framework
Shen Wang,Haimonti Dutta +1 more
TL;DR: Empirical results from the KDDCup competition on a large cluster indicates that significant scalability benefits can be obtained by using the parallel hierarchical clustering algorithm in addition to maintaining good cluster quality.
Proceedings ArticleDOI
Machine-Learning-Based Online Distributed Denial-of-Service Attack Detection Using Spark Streaming
TL;DR: This paper proposes a machine-learning based online Internet traffic monitoring system using Spark Streaming, a stream- processing-based big data framework, to detect DDoS attacks in real time, and shows the system performs well even for large Internet traffic.
Journal ArticleDOI
Discovering predictive ensembles for transfer learning and meta-learning
TL;DR: This article shows how data-tailored algorithms can be constructed from building blocks on small data sub-samples, and demonstrates how one particular template (simple ensemble of fast sigmoidal regression models) outperforms state-of-the-art approaches on the Airline data set.
Book ChapterDOI
Towards an Integrated Platform for Big Data Analysis
Mahdi Bohlouli,Frank Schulz,Lefteris Angelis,David Pahor,Ivona Brandic,David Atlan,Rosemary Tate +6 more
TL;DR: The amount of data in the world is expanding rapidly, and in order to understand these massive amounts of data, advanced visualization and data exploration techniques are required.
References
More filters
Journal ArticleDOI
Learning representations by back-propagating errors
TL;DR: Back-propagation repeatedly adjusts the weights of the connections in the network so as to minimize a measure of the difference between the actual output vector of the net and the desired output vector, which helps to represent important features of the task domain.
Journal ArticleDOI
MapReduce: simplified data processing on large clusters
Jeffrey Dean,Sanjay Ghemawat +1 more
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.
Journal ArticleDOI
An information-maximization approach to blind separation and blind deconvolution
TL;DR: It is suggested that information maximization provides a unifying framework for problems in "blind" signal processing and dependencies of information transfer on time delays are derived.
Journal ArticleDOI
Principal component analysis
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.