摘要翻译:
双聚类问题是将矩阵的行和列同时聚类,使得由一对行和列聚类诱导的每个子矩阵尽可能一致。本文通过在输入矩阵的行和列上独立地应用单向聚类算法来逼近最优双聚类。我们证明了对于0-1值矩阵,这种解在L1范数下的最坏情况逼近比为1+SQRT(2);对于实值矩阵,在L2范数下的最坏情况逼近比为2。
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英文标题:
《An Approximation Ratio for Biclustering》
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作者:
Kai Puolam\"aki, Sami Hanhij\"arvi, Gemma C. Garriga
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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:Data Structures and Algorithms 数据结构与算法
分类描述:Covers data structures and analysis of algorithms. Roughly includes material in ACM Subject Classes E.1, E.2, F.2.1, and F.2.2.
涵盖数据结构和算法分析。大致包括ACM学科类E.1、E.2、F.2.1和F.2.2中的材料。
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一级分类:Statistics 统计学
二级分类:Machine Learning 机器学习
分类描述:Covers machine learning papers (supervised, unsupervised, semi-supervised learning, graphical models, reinforcement learning, bandits, high dimensional inference, etc.) with a statistical or theoretical grounding
覆盖机器学习论文(监督,无监督,半监督学习,图形模型,强化学习,强盗,高维推理等)与统计或理论基础
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英文摘要:
The problem of biclustering consists of the simultaneous clustering of rows and columns of a matrix such that each of the submatrices induced by a pair of row and column clusters is as uniform as possible. In this paper we approximate the optimal biclustering by applying one-way clustering algorithms independently on the rows and on the columns of the input matrix. We show that such a solution yields a worst-case approximation ratio of 1+sqrt(2) under L1-norm for 0-1 valued matrices, and of 2 under L2-norm for real valued matrices.
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PDF链接:
https://arxiv.org/pdf/712.2682