本文選題：多視角聚類 + 非負矩陣分解　； 參考：《大連理工大學》2016年碩士論文

【摘要】：聚類一直是機器學習領域一個非常重要的內容,各種各樣高效的聚類算法被不斷地提出。另一方面,伴隨著網絡的發(fā)展,數(shù)據的獲取也變得越來越容易,一組相同的樣本經常被不同的特征描述。如何有效地利用不同維度空間下的數(shù)據來提高聚類的準確率是國內外眾多學者研究的課題,這就使得多視角聚類在近些年來取得了迅速的發(fā)展。鑒于非負矩陣分解(NMF)可解釋性強、算法實現(xiàn)簡單,基于NMF的聚類算法受到了廣泛地關注。不僅如此,實驗證明基于NMF的聚類算法無論在聚類的準確率還是在聚類的穩(wěn)定性方面都可以媲美其他的聚類算法。伴隨著NMF在聚類方面的成功,大批的學者將NMF應用到了多視角聚類中�；贜MF的多視角聚類算法繼承了NMF的優(yōu)點并較大幅度地提升了聚類的準確率。當然,基于NMF的多視角聚類算法也有著以下缺陷。首先,非負矩陣分解得到的分解結果并不唯一；其次非負矩陣分解的多視角聚類算法并沒有為每一個視角得到一個標準的正交基；最后非負矩陣分解并沒有保留數(shù)據的局部信息。為了彌補上述的三個不足,本文提出了一個基于雙約束非負矩陣分解(DCNMF)的多視角聚類算法。先在每個視角下的基矩陣施加正交約束,之后使用矩陣轉置相乘來進一步改進算法,以得到標準的正交基并避免正交性帶來的高算法復雜度。進一步的,為了保留不同視圖之間的局部信息,在目標函數(shù)中添加流形正則項。最后,本文提出了一個流形正則項參數(shù)的迭代規(guī)則,這樣做能夠平衡矩陣分解的重構誤差和流形正則項,此外還可以加快算法的收斂速度。之后文章從理論和實驗兩個方面證明了算法的收斂性,并設計實驗證明DCNMF的優(yōu)越性。與其他算法相比,DCNMF在算法的準確率和運行效率上都有顯著的提高。
[Abstract]:Clustering has been a very important part of the field of machine learning. All kinds of efficient clustering algorithms are constantly proposed. On the other hand, with the development of the network, the acquisition of data is becoming more and more easy. A group of identical samples are often described by different features. For example, how to effectively use data in different dimensional space Improving the accuracy of clustering is the subject of many scholars at home and abroad. This makes multi view clustering make rapid development in recent years. In view of the strong interpretability of the non negative matrix decomposition (NMF) and the simple implementation of the algorithm, the clustering algorithm based on NMF has received extensive attention. Not only that, the experiment proves that the clustering algorithm based on NMF is the same. The accuracy of clustering and the stability of clustering can be compared to other clustering algorithms. With the success of NMF in clustering, a large number of scholars have applied NMF to multi view clustering. The multi view clustering algorithm based on NMF inherits the advantages of NMF and greatly improves the accuracy of clustering. Of course, NMF based on NMF The multi view clustering algorithm also has the following defects. First, the decomposition results obtained by the non negative matrix decomposition are not unique; secondly, the multi view clustering algorithm with non negative matrix decomposition does not get a standard orthogonal basis for every angle of view; finally, the non negative matrix decomposition does not retain the local information of the data. In order to make up the above three In this paper, a multi view clustering algorithm based on double constraint non negative matrix decomposition (DCNMF) is proposed in this paper. First, the orthogonal constraints are applied to the base matrix in each view. Then the algorithm is further improved by using matrix transposed multiplication to obtain the standard orthogonal basis and avoid the high algorithmic complexity caused by orthogonality. The local information between different views is retained and the manifold regular items are added to the objective function. Finally, an iterative rule of the regular parameter of the manifold is proposed in this paper, which can balance the reconstruction error of the matrix decomposition and the regular term of the manifold. In addition, the convergence speed of the algorithm can be accelerated. After that, the article proves two aspects of theory and experiment. The convergence of the algorithm is clear, and the design experiment proves the superiority of DCNMF. Compared with other algorithms, DCNMF has a remarkable improvement in the accuracy and efficiency of the algorithm.

【學位授予單位】：大連理工大學
【學位級別】：碩士
【學位授予年份】：2016
【分類號】：TP181;TP311.13

【參考文獻】

相關期刊論文 前1條

1 黃鋼石,張亞非,陸建江,徐寶文;一種受限非負矩陣分解方法[J];東南大學學報(自然科學版);2004年02期

，

本文編號：1876156