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
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Generate‐map‐reduce: An extension to map‐reduce to support shared data and recursive computations
TL;DR: Generate‐Map‐Reduce (GMR) introduces a new Generate abstraction into the MapReduce framework that captures recursive computations, and illustrates how this caching helps in achieving significant speedup for iterative computations by modeling k‐means clustering.
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dSimpleGraph: A Novel Distributed Clustering Algorithm for Exploring Very Large Scale Unknown Data Sets
TL;DR: This paper proposes and implements a novel micro-cluster based distributed clustering algorithm called dSimpleGraph, which can efficiently cluster data on the local machines and can easily generate a determined global view from local views.
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Parallelizing convolutional neural network for the handwriting recognition problems with different architectures
TL;DR: The basic idea of the method is to use the multi-process to process the training samples in parallel, to exchange the training results and to get the final weight parameters, which significantly improves the efficiency of CNN in the hand written numeral recognition.
Randomized algorithms for scalable machine learning
Michael I. Jordan,Ariel Kleiner +1 more
TL;DR: This work proposes novel randomized algorithms for two broad classes of problems that arise in machine learning and statistics: estimator quality assessment and semidefinite programming and presents Random Conic Pursuit, a procedure that solves semide finite programs via repeated optimization over randomly selected two-dimensional subcones of the positive semideFinite cone.
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Co-Clustering Algorithm: Batch, Mini-Batch, and Online
Hyuk Cho,Min Kyung An +1 more
TL;DR: This paper develops an online incremental co-clustering algorithm that can update both row and column clustering statistics on the fly only for each available data point; thus, the proposed algorithm can handle stream data collected from sensor networks or handheld devices.
References
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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.