Proposal for GSoC2010 (Linear SVM for Mahout)
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Key: MAHOUT-334
URL: https://issues.apache.org/jira/browse/MAHOUT-334
Project: Mahout
Issue Type: Task
Reporter: zhao zhendong
Title/Summary: Linear SVM Package (LIBLINEAR) for Mahout
Student: Zhen-Dong Zhao
Student e-mail: zha...@comp.nus.edu.sg
Student Major: Multimedia Information Retrieval /Computer Science
Student Degree: Master Student Graduation: NUS'10
Organization: Hadoop
0 Abstract
Linear Support Vector Machine (SVM) is pretty useful in some applications with
large-scale datasets or datasets with high dimension features. This proposal
will port one of the most famous linear SVM solvers, say, LIBLINEAR [1] to
mahout with unified interface as same as Pegasos [2] @ mahout, which is another
linear SVM solver and almost finished by me. Two distinct con tributions would
be: 1) Introduce LIBLINEAR to Mahout; 2) Unified interfaces for linear SVM
classifier.
1 Motivation
As one of TOP 10 algorithms in data mining society [3], Support Vector Machine
is very powerful Machine Learning tool and widely adopted in Data Mining,
Pattern Recognition and Information Retrieval domains.
The SVM training procedure is pretty slow, however, especially on the case with
large-scale dataset. Nowadays, several literatures propose SVM solvers with
linear kernel that can handle large-scale learning problem, for instance,
LIBLINEAR [1] and Pegasos [2]. I have implemented a prototype of linear SVM
classifier based on Pegasos [2] for Mahout (issue: Mahout-232). Nevertheless, as
the winner of ICML 2008 large-scale learning challenge (linear SVM track
(http://largescale.first.fraunhofer.de/summary/), LIBLINEAR [1] suppose to be
incorporated in Mahout too. Currently, LIBLINEAR package supports:
(1) L2-regularized classifiers L2-loss linear SVM, L1-loss linear SVM, and
logistic regression (LR)
(2) L1-regularized classifiers L2-loss linear SVM and logistic regression (LR)
Main features of LIBLINEAR are following:
(1) Multi-class classification: 1) one-vs-the rest, 2) Crammer & Singer
(2) Cross validation for model selection
(3) Probability estimates (logistic regression only)
(4) Weights for unbalanced data
All the functionalities suppose to be implemented except probability estimates
and weights for unbalanced data (If time permitting, I would like to do so).
2 Unified Interfaces
Linear SVM classifier based on Pegasos package on Mahout already can provide
such functionalities: (http://issues.apache.org/jira/browse/MAHOUT-232)
(1) Sequential Binary Classification (Two-class Classification), includes
sequential training and prediction;
(2) Sequential Regression;
(3) Parallel & Sequential Multi-Classification, includes One-vs.-One and
One-vs.-Others schemes.
Apparently, the functionalities of Pegasos package on Mahout and LIBLINEAR are
quite similar to each other. As aforementioned, in this section I will
introduce an unified interfaces for linear SVM classifier on Mahout, which will
incorporate Pegasos, LIBLINEAR.
The unfied interfaces has two main parts: 1) Dataset loader; 2) Algorithms. I
will introduce them separately.
2.1 Data Handler
The dataset can be stored on personal computer or on Hadoop cluster. This
framework provides high performance Random Loader, Sequential Loader for
accessing large-scale data.
2.2 Sequential Algorithms
Sequential Algorithms will include binary classification, regression based on
Pegasos and LIBLINEAR with unified interface.
2.3 Parallel Algorithms
It is widely accepted that to parallelize binary SVM classifier is hard. For
multi-classification, however, the coarse-grained scheme (e.g. each Mapper or
Reducer has one independent SVM binary classifier) is easier to achieve great
improvement. Besides, cross validation for model selection also can take
advantage of such coarse-grained parallelism. I will introduce a unified
interface for all of them.
3 Biography:
I am a graduating masters student in Multimedia Information Retrieval System
from National University of Singapore. My research has involved the large-scale
SVM classifier.
I have worked with Hadoop and Map Reduce since one year ago, and I have
dedicated lots of my spare time to Sequential SVM (Pegasos) based on Mahout
(http://issues.apache.org/jira/browse/MAHOUT-232). I have taken part in setting
up and maintaining a Hadoop cluster with around 70 nodes in our group.
4 Timeline:
Weeks 1-4 (May 24 ~ June 18): Implement binary classifier
Weeks 5-7 (June 21 ~ July 12): Implement parallel multi-class classification
and Implement cross validation for model selection.
Weeks 8 (July 12 ~ July 16): Summit of mid-term evaluation
Weeks 9 - 11 (July 16 ~ August 9): Interface re-factory and performance turning
Weeks 11 - 12 (August 9 ~ August 16): Code cleaning, documents and testing.
5 References
[1] Rong-En Fan, Kai-Wei Chang, Cho-Jui Hsieh, Xiang-Rui Wang, and Chih-Jen
Lin. Liblinear: A library for large linear classification. J. Mach. Learn. Res.,
9:1871-1874, 2008.
[2] Shai Shalev-Shwartz, Yoram Singer, and Nathan Srebro. Pegasos: Primal
estimated sub-gradient solver for svm. In ICML '07: Proceedings of the 24th
international conference on Machine learning, pages 807-814, New York, NY, USA,
2007. ACM.
[3] Xindong Wu, Vipin Kumar, J. Ross Quinlan, Joydeep Ghosh, Qiang Yang,
Hiroshi Motoda, Geoffrey J. McLachlan, Angus Ng, Bing Liu, Philip S. Yu, Zhi-Hua
Zhou, Michael Steinbach, David J. Hand, and Dan Steinberg. Top 10 algorithms in
data mining. Knowl. Inf. Syst., 14(1):1-37, 2007.
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