2020/04/27 by Qiang Liu, Liu, Qiang, Zhaocheng Liu +3 · 2 citations
Computer Science · Mathematics · #Advanced Image and Video Retrieval Techniques #FOS: Computer and information sciences #Image Retrieval and Classification Techniques #Information Retrieval (cs.IR) #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Neural Networks and Applications #cs.IR #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.2004.12602
arxiv created 2020/04/27 · openalex publication_date 2020/04/27 · arxiv updated 2020/04/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
When dealing with continuous numeric features, we usually adopt feature discretization. In this work, to find the best way to conduct feature discretization, we present some theoretical analysis, in which we focus on analyzing correctness and robustness of feature discretization. Then, we propose a novel discretization method called Local Linear Encoding (LLE). Experiments on two numeric datasets show that, LLE can outperform conventional discretization method with much fewer model parameters.