6533b854fe1ef96bd12ae9b4
RESEARCH PRODUCT
Linear Feature Extraction for Ranking
Shuaiqiang WangJari VeijalainenZhaochun RenGaurav PandeyMaarten De Rijkesubject
dimension reductionComputer scienceFeature extractionMathematicsofComputing_NUMERICALANALYSISFeature selectiontiedonhakujärjestelmät02 engineering and technologyLibrary and Information SciencesRanking (information retrieval)Matrix (mathematics)Transformation matrix020204 information systemsalgoritmit0202 electrical engineering electronic engineering information engineeringtiedonhakulearning to rankbusiness.industryfeature extractionPattern recognitionkoneoppiminenPattern recognition (psychology)Benchmark (computing)020201 artificial intelligence & image processingLearning to rankArtificial intelligencebusinessInformation Systemsdescription
We address the feature extraction problem for document ranking in information retrieval. We then propose LifeRank, a Linear feature extraction algorithm for Ranking. In LifeRank, we regard each document collection for ranking as a matrix, referred to as the original matrix. We try to optimize a transformation matrix, so that a new matrix (dataset) can be generated as the product of the original matrix and a transformation matrix. The transformation matrix projects high-dimensional document vectors into lower dimensions. Theoretically, there could be very large transformation matrices, each leading to a new generated matrix. In LifeRank, we produce a transformation matrix so that the generated new matrix can match the learning to rank problem. Extensive experiments on benchmark datasets show the performance gains of LifeRank in comparison with state-of-the-art feature selection algorithms.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2018-12-01 |