@phdthesis{oai:doshisha.repo.nii.ac.jp:00001348,
 author = {阿部, 寛康 and Abe, Hiroyasu},
 month = {2017-04-14, 2018-02-23},
 note = {非負値行列因子分解(NMF)は，全要素が非負であるデータ行列に対する行列分解法である．本論文では，実在するデータ行列に頻繁に見られる特徴や解釈容易性の向上を考慮に入れ，探索的にデータ分析を行うためのNMFの拡張について論じている．具体的には，零過剰行列や外れ値を含む行列を扱うための確率分布やダイバージェンス，さらには分解結果である因子行列の数や因子行列への直交制約について述べている．, Nonnegative matrix factorization (NMF) is a matrix decomposition technique to analyze nonnegative data matrices, which are matrices of which all elements are nonnegative. In this thesis, we discuss extensions of NMF for exploratory data analysis considering common features of a real nonnegative data matrix and an easy interpretation. In particular, we discuss probability distributions and divergences for zero-inflated data matrix and data matrix with outliers, two-factor vs. three-factor, and orthogonal constraint to factor matrices., application/pdf},
 title = {Extensions of nonnegative matrix factorization for exploratory data analysis},
 year = {},
 yomi = {アベ, ヒロヤス}
}