結(jié)合弱監(jiān)督信息的凸聚類研究
發(fā)布時(shí)間：2018-09-19 09:41
【摘要】：基于目標(biāo)函數(shù)的聚類是一類重要的聚類分析技術(shù),其中幾乎所有算法均是經(jīng)非凸目標(biāo)的優(yōu)化建立,因而難以保證全局最優(yōu)并對(duì)初始值敏感.近年提出的凸聚類通過優(yōu)化凸目標(biāo)函數(shù)克服了上述不足,同時(shí)獲得了相對(duì)更穩(wěn)定的解.當(dāng)現(xiàn)實(shí)中存在輔助信息(典型的如必連和/或不連約束)可資利用時(shí),通過將其結(jié)合到相應(yīng)目標(biāo)所得優(yōu)化模型已證明能有效提高聚類性能,然而,現(xiàn)有通過在目標(biāo)函數(shù)中添加約束懲罰項(xiàng)的常用結(jié)合方式往往會(huì)破壞其原有凸目標(biāo)的凸性.鑒于此,提出了一種新的結(jié)合此類弱監(jiān)督輔助信息的凸聚類算法.其實(shí)現(xiàn)關(guān)鍵是代替在目標(biāo)函數(shù)中添加約束,而是通過對(duì)目標(biāo)函數(shù)中距離度量的改造以保持凸性,由此既保持了原凸聚類的優(yōu)勢(shì)同時(shí)有效提高了聚類性能.
[Abstract]:Clustering based on objective function is an important clustering analysis technique in which almost all of the algorithms are established by the optimization of non-convex objects so it is difficult to ensure the global optimization and be sensitive to the initial value. The convex clustering proposed in recent years overcomes the above shortcomings by optimizing convex objective functions and obtains a more stable solution at the same time. When there are auxiliary information (such as mandatory and / or non-connected constraints) available in reality, it has been proved that the optimization model can effectively improve the clustering performance by combining it with the corresponding target optimization model, however, The commonly used combination of constraint penalty items in the objective function will destroy the convexity of its original convex object. In view of this, a new convex clustering algorithm combining this kind of weakly supervised auxiliary information is proposed. Instead of adding constraints to the objective function, the key is to maintain the convexity by modifying the distance measure in the objective function, which not only preserves the advantages of the original convex clustering, but also improves the clustering performance effectively.
【作者單位】： 南京航空航天大學(xué)計(jì)算機(jī)科學(xué)與技術(shù)學(xué)院;
【基金】：國(guó)家自然科學(xué)基金項(xiàng)目(61672281)~~
【分類號(hào)】：TP311.13
本文編號(hào)：2249762
[Abstract]:Clustering based on objective function is an important clustering analysis technique in which almost all of the algorithms are established by the optimization of non-convex objects so it is difficult to ensure the global optimization and be sensitive to the initial value. The convex clustering proposed in recent years overcomes the above shortcomings by optimizing convex objective functions and obtains a more stable solution at the same time. When there are auxiliary information (such as mandatory and / or non-connected constraints) available in reality, it has been proved that the optimization model can effectively improve the clustering performance by combining it with the corresponding target optimization model, however, The commonly used combination of constraint penalty items in the objective function will destroy the convexity of its original convex object. In view of this, a new convex clustering algorithm combining this kind of weakly supervised auxiliary information is proposed. Instead of adding constraints to the objective function, the key is to maintain the convexity by modifying the distance measure in the objective function, which not only preserves the advantages of the original convex clustering, but also improves the clustering performance effectively.
【作者單位】： 南京航空航天大學(xué)計(jì)算機(jī)科學(xué)與技術(shù)學(xué)院;
【基金】：國(guó)家自然科學(xué)基金項(xiàng)目(61672281)~~
【分類號(hào)】：TP311.13
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，本文編號(hào)：2249762
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