Flexible Subspace Clustering: A Joint Feature Selection and K-Means Clustering Framework
نویسندگان
چکیده
منابع مشابه
Subspace K-means clustering.
To achieve an insightful clustering of multivariate data, we propose subspace K-means. Its central idea is to model the centroids and cluster residuals in reduced spaces, which allows for dealing with a wide range of cluster types and yields rich interpretations of the clusters. We review the existing related clustering methods, including deterministic, stochastic, and unsupervised learning app...
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Unions of subspaces provide a powerful generalization of single subspace models for collections of high-dimensional data; however, learning multiple subspaces from data is challenging due to the fact that segmentation—the identification of points that live in the same subspace—and subspace estimation must be performed simultaneously. Recently, sparse recovery methods were shown to provide a pro...
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ژورنال
عنوان ژورنال: Big Data Research
سال: 2021
ISSN: 2214-5796
DOI: 10.1016/j.bdr.2020.100170