文章基本信息

标题：Unsupervised Multi-Level Non-Negative Matrix Factorization Model: Binary Data Case
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作者：Qingquan Sun ; Peng Wu ; Yeqing Wu 等
期刊名称：Journal of Information Security
印刷版ISSN：2153-1234
电子版ISSN：2153-1242
出版年度：2012
卷号：3
期号：4
页码：245-250
DOI：10.4236/jis.2012.34031
出版社：Scientific Research Publishing
摘要：Rank determination issue is one of the most significant issues in non-negative matrix factorization (NMF) research. However, rank determination problem has not received so much emphasis as sparseness regularization problem. Usually, the rank of base matrix needs to be assumed. In this paper, we propose an unsupervised multi-level non-negative matrix factorization model to extract the hidden data structure and seek the rank of base matrix. From machine learning point of view, the learning result depends on its prior knowledge. In our unsupervised multi-level model, we construct a three-level data structure for non-negative matrix factorization algorithm. Such a construction could apply more prior knowledge to the algorithm and obtain a better approximation of real data structure. The final bases selection is achieved through L₂-norm optimization. We implement our experiment via binary datasets. The results demonstrate that our approach is able to retrieve the hidden structure of data, thus determine the correct rank of base matrix.
关键词：Non-Negative Matrix Factorization; Bayesian Model; Rank Determination; Probabilistic Model