Component-wise robust linear fuzzy clustering for collaborative filtering
Component-wise robust linear fuzzy clustering for collaborative filtering
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DOI：
10.1016/j.ijar.2004.02.001
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发表时间：
2004-09
期刊：
影响因子：
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通讯作者：
Katsuhiro Honda;H. Ichihashi
中科院分区：
文献类型：
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作者：
Katsuhiro Honda;H. Ichihashi
自动协同过滤是一种减少信息过载的流行技术，其任务是预测数据矩阵中的缺失值。提取局部线性模型是预测缺失值的有用技术。通过改进的线性模糊聚类算法来估计具有高维不完整数据集局部结构的线性模型。模糊 c 变量 (FCV) 是一种线性模糊聚类算法，它将局部主成分向量估计为跨越聚类原型的向量。然而，最小二乘技术通常无法考虑实际应用中常见的“异常值”。在本文中，提出了一种使 FCV 算法对样本内异常值具有鲁棒性的技术。基于数据矩阵的较低秩近似的目标函数通过类似于 FCM 类型迭代过程的鲁棒 M 估计算法最小化。在数值实验中，通过使用稳健的局部线性模型预测缺失值，可以提高过滤系统的诊断能力。
Automated collaborative filtering is a popular technique for reducing information overload and the task is to predict missing values in a data matrix. Extraction of local linear models is a useful technique for predicting the missing values. Linear models featuring local structures of the high-dimensional incomplete data set are estimated by a modified linear fuzzy clustering algorithm. Fuzzy c-varieties (FCV) is a linear fuzzy clustering algorithm that estimates local principal component vectors as the vectors spanning prototypes of clusters. Least squares techniques, however, often fail to account for “outliers”, which are common in real applications. In this paper, a technique for making the FCV algorithm robust to intra-sample outliers is proposed. The objective function based on the lower rank approximation of the data matrix is minimized by a robust M-estimation algorithm that is similar to FCM-type iterative procedures. In numerical experiments, the diagnostic power of the filtering system is shown to be improved by predicting missing values using robust local linear models.