基于0矩阵分解的相关因子分析

R. Belohlávek, Vilém Vychodil
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引用次数: 0

摘要

本文给出了描述对象及其模糊属性的矩阵的分解结果。矩阵的条目是真度,例如,实单位区间[0,1]内的数。一般来说,矩阵项可以是完全残馀格中的元素。我们提出了一种新的方法来分解这种矩阵,这种方法是基于使用所谓的形式概念作为因子。分解一个n乘以m的对象属性矩阵I意味着将I分解成一个n乘以k的对象因子矩阵a和k乘以m的因子属性矩阵B的乘积a的积B。此外,我们希望因子的k个数尽可能少。本文考虑的乘积0是模糊关系的最大-t-范数组合所对应的众所周知的乘积。我们着重对我们提出的方法进行理论分析。我们证明了几个结果,例如,一个结果表明,我们的方法提供了最好的分解,因为它导致的因子数量最少。此外,我们给出了一个说明性的例子。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Relational Factor Analysis with o-Matrix Decomposition
The paper presents results on factorization of matrices describing objects and their fuzzy attributes. Entries of the matrices are truth degrees, e.g., numbers from the real unit interval [0, 1]. In general, matrix entries can be elements from a complete residuated lattice. We propose a novel method to factorize such matrices which is based on using so-called formal concepts as factors. To factorize an n times m object-attribute matrix I means to decompose I into a product A omicron B of an n times k object-factor matrix A and an k times m factor-attribute matrix B. In addition, we want the number k of factors as small as possible. The product o we consider in this paper is the well-known product corresponding to max-t-norm composition of fuzzy relations. We focus on theoretical analysis of the method we propose. We prove several results, e.g., a result which says that our method provides the best factorization in that it leads to the smallest number of factors. In addition, we present an illustrative example.
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