A Latent Factor Analysis-Based Approach to Online Sparse Streaming Feature Selection
针对在线流特征选择中特征稀疏且含缺失数据的问题，提出一种基于潜在因子分析的算法LOSSA，先预估计缺失值再选择特征，实验表明能显著提升选择质量。
Online streaming feature selection (OSFS) has attracted extensive attention during the past decades. Current approaches commonly assume that the feature space of fixed data instances dynamically increases without any missing data. However, this assumption does not always hold in many real applications. Motivated by this observation, this study aims to implement online feature selection from sparse streaming features, i.e., features flow in one by one with missing data as instance count remains fixed. To do so, this study proposes a latent-factor-analysis-based online sparse-streaming-feature selection algorithm (LOSSA). Its main idea is to apply latent factor analysis to pre-estimate missing data in sparse streaming features before conducting feature selection, thereby addressing the missing data issue effectively and efficiently. Theoretical and empirical studies indicate that LOSSA can significantly improve the quality of OSFS when missing data are encountered in target instances.