{"id":18015525,"url":"https://github.com/wmkouw/flda","last_synced_at":"2025-03-26T18:31:20.438Z","repository":{"id":25800656,"uuid":"29239471","full_name":"wmkouw/flda","owner":"wmkouw","description":"Feature-level domain 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Feature-level domain adaptation\r\n\r\nThis repository contains MATLAB code accompanying the paper:\r\n\r\n\"Feature-level domain adaptation.\"\r\n\r\nwhich is published in the Journal of Machine Learning Research 2016 ([pdf](http://www.jmlr.org/papers/v17/15-206.html)/[preprint](https://arxiv.org/abs/1512.04829)).\r\n\r\nFor a cleaner implementation of flda as well as a translation into Python, see my library on transfer learners and domain-adaptive classifiers: [libTLDA](https://github.com/wmkouw/libTLDA).\r\n\r\n## Installation\r\nClone the repository (bash):\r\n```shell\r\ngit clone https://github.com/wmkouw/flda\r\n```\r\nInstallation consists of adding the repository to your path (matlab):\r\n```\r\naddpath(genpath('./flda'))\r\n```\r\n\r\n### Dependencies\r\nFlda depends on  [minFunc](http://www.cs.ubc.ca/~schmidtm/Software/minFunc.html) and [libSVM](https://www.csie.ntu.edu.tw/~cjlin/libsvm/).\r\nFirst download and extract them (bash):\r\n```\r\nwget http://www.cs.ubc.ca/~schmidtm/Software/minFunc_2012.zip -O minFunc.zip\r\nunzip minFunc.zip\r\n\r\nwget http://www.csie.ntu.edu.tw/~cjlin/cgi-bin/libsvm.cgi?+http://www.csie.ntu.edu.tw/~cjlin/libsvm+zip -O libSVM.zip\r\nunzip libSVM.zip\r\n```\r\n\r\nThen add them to your path (matlab):\r\n```\r\naddpath(genpath('./minFunc_2012'))\r\naddpath(genpath('./libSVM-3.22'))\r\n```\r\n\r\n## Usage\r\nRepo contains the following folders:\r\n- __experiment-*__: contains scripts for running experiments reported in the paper.\r\n- __data__: contains the digits, spam, office, imdb and amazon data sets.\r\n- __util__: contains utility functions and algorithms.\r\n\r\nTo start an experiment, call the corresponding experiment function (matlab):\r\n```\r\ncd experiment-amazon/\r\nrun_daexp_amazon('flda_log_b')\r\n```\r\nOptions for classifiers are:\r\n- 'flda_log_b': flda with logistic loss and blankout transfer model\r\n- 'flda_log_d': flda with logistic loss and dropout transfer model\r\n- 'flda_qd_b': flda with quadratic loss and blankout transfer model\r\n- 'flda_qd_d': flda with quadratic loss and dropout transfer model\r\n- 'gfk_knn': geodesic flow kernel with a k-nearest-neighbour classifier\r\n- 'tca_svm': transfer component analysis with a support vector machine\r\n- 'sa_svm': subspace alignment with a support vector machine\r\n- 'kmm': kernel mean matching with importance-weighted logistic regression\r\n- 'scl': structural correspondence learning with logistic regression\r\n\r\n\r\n### Contact\r\nBugs, comments and questions can be submitted to the issues tracker.\r\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fwmkouw%2Fflda","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fwmkouw%2Fflda","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fwmkouw%2Fflda/lists"}