摘要翻译:
本文研究了稀疏主成分分析(PCA)在聚类和特征选择问题中的应用。稀疏PCA寻求稀疏因子,或数据变量的线性组合,解释数据中的最大方差量,而只有有限数量的非零系数。PCA通常被用作一种简单的聚类技术,稀疏因子允许我们在这里用一组减少的变量来解释聚类。我们首先简要介绍了稀疏PCA的研究现状,并详细介绍了D'Aspremont等人提出的稀疏PCA算法的实现过程。(2005年)。然后,我们将这些结果应用于生物学中一些经典的聚类和特征选择问题。
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英文标题:
《Clustering and Feature Selection using Sparse Principal Component
Analysis》
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作者:
Ronny Luss, Alexandre d'Aspremont
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最新提交年份:
2008
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分类信息:
一级分类:Computer Science 计算机科学
二级分类:Artificial Intelligence 人工智能
分类描述:Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域,除了视觉、机器人、机器学习、多智能体系统以及计算和语言(自然语言处理),这些领域有独立的学科领域。特别地,包括专家系统,定理证明(尽管这可能与计算机科学中的逻辑重叠),知识表示,规划,和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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一级分类:Computer Science 计算机科学
二级分类:Machine Learning 机器学习
分类描述:Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.
关于机器学习研究的所有方面的论文(有监督的,无监督的,强化学习,强盗问题,等等),包括健壮性,解释性,公平性和方法论。对于机器学习方法的应用,CS.LG也是一个合适的主要类别。
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一级分类:Computer Science 计算机科学
二级分类:Mathematical Software 数学软件
分类描述:Roughly includes material in ACM Subject Class G.4.
大致包括ACM学科类G.4的材料。
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英文摘要:
In this paper, we study the application of sparse principal component analysis (PCA) to clustering and feature selection problems. Sparse PCA seeks sparse factors, or linear combinations of the data variables, explaining a maximum amount of variance in the data while having only a limited number of nonzero coefficients. PCA is often used as a simple clustering technique and sparse factors allow us here to interpret the clusters in terms of a reduced set of variables. We begin with a brief introduction and motivation on sparse PCA and detail our implementation of the algorithm in d'Aspremont et al. (2005). We then apply these results to some classic clustering and feature selection problems arising in biology.
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PDF链接:
https://arxiv.org/pdf/0707.0701