模式识别与人工智能
模式識彆與人工智能
모식식별여인공지능
Moshi Shibie yu Rengong Zhineng
2013年
3期
300-306
,共7页
非负矩阵分解(NMF)%受限%图正则化%几何结构%聚类
非負矩陣分解(NMF)%受限%圖正則化%幾何結構%聚類
비부구진분해(NMF)%수한%도정칙화%궤하결구%취류
Non-Negative Matrix Factorization (NMF)%Constraint%Graph Regularization%Geometrical Structure%Clustering
非负矩阵分解(NMF)是一种非常有效的图像表示方法,已被广泛应用到模式识别领域.针对NMF算法是无监督学习算法,无法同时考虑样本类别信息和固有几何结构信息的缺点,提出一种基于图正则化的受限非负矩阵分解(GRCNMF)的算法.该算法利用硬约束保持样本的类别信息,增强算法的鉴别能力,同时还利用近邻图来保持样本间固有的几何结构.通过在COIL20和ORL图像库中的聚类实验结果表明GRCNMF优于其它几种算法,说明GRCNMF的有效性.
非負矩陣分解(NMF)是一種非常有效的圖像錶示方法,已被廣汎應用到模式識彆領域.針對NMF算法是無鑑督學習算法,無法同時攷慮樣本類彆信息和固有幾何結構信息的缺點,提齣一種基于圖正則化的受限非負矩陣分解(GRCNMF)的算法.該算法利用硬約束保持樣本的類彆信息,增彊算法的鑒彆能力,同時還利用近鄰圖來保持樣本間固有的幾何結構.通過在COIL20和ORL圖像庫中的聚類實驗結果錶明GRCNMF優于其它幾種算法,說明GRCNMF的有效性.
비부구진분해(NMF)시일충비상유효적도상표시방법,이피엄범응용도모식식별영역.침대NMF산법시무감독학습산법,무법동시고필양본유별신식화고유궤하결구신식적결점,제출일충기우도정칙화적수한비부구진분해(GRCNMF)적산법.해산법이용경약속보지양본적유별신식,증강산법적감별능력,동시환이용근린도래보지양본간고유적궤하결구.통과재COIL20화ORL도상고중적취류실험결과표명GRCNMF우우기타궤충산법,설명GRCNMF적유효성.
@@@@Non-negative matrix factorization ( NMF) is an effective image representation method and has considerable attention in pattern recognition. The NMF is an unsupervised learning algorithm which can not take into account the label information and the intrinsic geometry structure simultaneously. In this paper, a matrix decomposition method called graph-regularized constrained non-negative matrix factorization ( GRCNMF) is proposed, which preserves the label information with resorting to hard constraints, and hence the discriminating ability is improved. Meanwhile, a neighbors graph preserves the intrinsic geometrical structure of the data. The clustering experiments on the COIL20 and ORL image database demonstrate the effectiveness of the GRCNMF compared to other approaches.
