面向真正无监督的特征选择评估
Towards Truly Unsupervised Evaluation of Feature Selection -- Extended Version
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中文总结 AI 辅助
本文针对现有无监督特征选择评估方法的缺陷，提出了一种无需标签信息、结合无监督主成分分析与最优传输的真正无监督评估框架，解决了现有评估方法的监督性质问题。
中文摘要 AI 辅助
特征选择是数据挖掘中最重要且最基础的任务之一，已有一系列方法及成熟的评估技术用于衡量特定方法的质量。大多数常用于无监督特征选择算法评估的方法存在关键设计缺陷，使其无监督性质受到质疑。本文对已有的所谓无监督评估技术进行了批判性讨论，阐明了它们并非真正无监督，充其量是在无监督下游任务下的监督评估的原因。我们还提出了一种新颖的真正无监督评估框架，无需任何标签信息即可衡量特征选择算法的质量。该框架利用无监督主成分分析（unsupervised Principal Component Analysis）和最优传输（optimal transport），以真正无监督的方式衡量特征选择方法的质量。
英文摘要
Feature selection is one of the most important and fundamental tasks in data mining, tackled by a family of methods with an established set of evaluation techniques to measure the quality of a specific method. Most of the methods commonly used for the unsupervised evaluation of feature selection algorithms suffer from critical design flaws which question their unsupervised nature. In this paper, we provide a critical discussion on the established allegedly unsupervised evaluation techniques, and shed light on the reasons why they are not truly unsupervised but, at best, supervised evaluation under an unsupervised downstream task. We also propose a novel, truly unsupervised evaluation framework to measure the quality of the feature selection algorithms without any form of information about the labels. The proposed framework utilizes unsupervised Principal Component Analysis, and optimal transport to measure the quality of the feature selection methods in a truly unsupervised manner.
发表机构
- University of Southern Denmark（南丹麦大学）
机构由 AI 辅助整理，请以论文原文为准。
