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.

中文翻译：

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线性特征提取以进行排名

我们解决了信息检索中文档排名的特征提取问题。然后，我们提出LifeRank中，李近˚F eature Ë xtraction算法排名ING。在LifeRank中，我们将每个要排序的文档集合视为一个矩阵，称为原始矩阵。我们尝试优化转换矩阵，以便可以生成新矩阵（数据集）作为原始矩阵和转换矩阵的乘积。变换矩阵将高维文档向量投影到低维。从理论上讲，可能会有非常大的转换矩阵，每个转换矩阵都会导致生成新的矩阵。在LifeRank中，我们生成一个转换矩阵，以便生成的新矩阵可以匹配学习排序问题。在基准数据集上进行的大量实验表明，与最新的特征选择算法相比，LifeRank的性能有所提高。