全局结构保存和基于自我呈现的监督特征选择

Qing Ye, Yaxin Sun
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引用次数: 0

摘要

特征选择的目的是从高维数据中选取一个特征子集,从而克服维度诅咒,以便进行下一步处理。然而,特征选择本身也可能面临维度诅咒。为了克服上述问题,本文根据人类日常生活中的处理过程设计了一个新的特征选择框架。在我们的日常生活中,要评价一个应聘者的工作能力,需要同时评价应聘者的相关专业知识和综合能力。事实上,一个只有良好专业知识的应聘者往往很难解决工作中的新问题。基于上述分析,在我们设计的新框架中,特征的选取是通过评价其全局结构保持能力和自我表现能力来实现的,而这两种能力分别与评价候选人的专业知识和综合能力相似。因此,所选特征可以适应测试数据的较大变化。实验验证了特征选择的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Global Structure Preservation and Self-Representation-Based Supervised Feature Selection
Feature selection aims to select a subset of features from high-dimensional data, which can overcome the curse of dimensionality for the next dealing steps. However, the feature selection itself could face the curse of dimensionality. To overcome the above problem, in this paper, a new feature selection framework is designed according to a human processing in our daily life. In our daily life, to evaluate a candidate's ability to work, the related professional knowledge and the comprehensive ability of a candidate should be both evaluated. Actually, a candidate only with good professional knowledge often hardly solves new problems in the work. Based on the above analysis, in our new designed framework, the features are selected by evaluating its ability of global structure preservation and self-representation, which are respectively similar to the professional knowledge and comprehensive ability in evaluating candidate. As a result, the selected features can accommodate larger changes in test data. The conducted experiments validate the effectiveness of our feature selection.
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