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SOL - Sparse Online Learning Toolbox
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About SOL
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This executable is provided with the paper submission of "Massive-scale Online Feature Selection for Sparse
Ultra-high Dimensinal Data". SOL is an executable for large-scale sparse online
learning or online feature selection, which
consists of a family of efficient and scalable online learning
algorithms for large-scale online classification tasks. We have offered
easy-to-use command-line tools and examples. 

Specifically, SOL consists of First order online feature selection algorithms,
- PET: Perceptron with truncation
- FOFS: first order online feature selection (Jialei et al., 2012)

and Second order online feature selection
- SOFS: confidence weighted second order online feature selection

This file briefly explains the usage of SOL. 

To get started, please read the ``Quick Start'' section first.

Quick Start
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Running SOL without any arguments or with '--help' will produce a message which briefly explains each argument. 

We provide an example to show how to use SOL and explain the details of how SOL works.
The dataset we use will be `pcmac`. Note that only LibSVM datasets are supported by default.

The command for training with default algorithm is as the following shows.
    
    SOL.exe -i data\pcmac_train 

If we want to use the proposed algorithm to select 1000 features, it can be called by:

    SOL.exe -i data\pcmac_train -m SOFS -r 1 -k 1000 -loss SquaredHinge

In our experiments, we normalize each sample to make ||x||_2 bounded.  The
command line is:
    SOL.exe -i data\pcmac_train -m SOFS -r 1 -k 1000 -loss SquaredHinge -norm

To train and test the model:
    SOL.exe -i data\pcmac_train -t data\pcmac_test -m SOFS -r 1 -k 1000 -loss SquaredHinge -norm

To obtain the list of loss functions:
    SOL.exe -help loss

To obtain  the list of algorithms
    SOL.exe -help model

Additional Information
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Please do not distribute.

Contact
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chhoi@smu.edu.sg
