Intra-Topic Variability Normalization based on Linear Projection for Topic Classification
Quan Liu, Wu Guo, Zhen-Hua Ling, Hui Jiang, Yu Hu · 2016
This paper proposes a variability normalization algorithm to reduce the variability between intra-topic documents for topic classification.Firstly, an optimization problem is constructed based on linear variability removable assumption.Secondly, a new feature space for document representation is found by solving the optimization problem with kernel principle component analysis (KPCA).Finally, effective feature transformation is taken through linear projection.As for experiments, state-of-the-art SVM and KNN algorithm are adopted for topic classification respectively.Experimental results on a free-style conversational corpus show that the proposed variability normalization algorithm for topic classification achieves 3.8% absolute improvement for micro-F 1 measure.