7步走写摘要: GMC: Graph-based Multi-view Clustering

本篇论文发表于IEEE Transactions on Knowledge and Data Engineering (2019)，CCF A类，中科院二区。

基于多视图图的聚类旨在为多视图数据提供聚类解决方案。然而，大多数现有方法没有充分考虑不同视图的权重，并且需要额外的聚类步骤来产生最终的聚类。他们通常还基于所有视图的固定图相似度矩阵来优化目标。在本文中，我们提出了一种基于图形的通​​用多视图聚类（GMC）来解决这些问题。 GMC获取所有视图的数据图矩阵并将其融合以生成统一的矩阵。统一矩阵又改善了每个视图的数据图矩阵，并直接给出了最终的聚类。 GMC的关键新颖之处在于其学习方法，它可以以相互增强的方式帮助学习每个视图图形矩阵和统一矩阵。一种新颖的多视图融合技术可以自动对每个数据图矩阵进行加权，以得出统一的矩阵。在不引入调整参数的情况下，秩约束也施加在统一矩阵的拉普拉斯矩阵上，这有助于将数据点自然地划分为所需数量的簇。提出了一种交替迭代优化算法来优化目标函数。实验结果表明，提出的方法明显优于最新的基准。	Multi-view graph-based clustering aims to provide clustering solutions to multi-view data. However, most existing methods do not give sufficient consideration to weights of different views and require an additional clustering step to produce the final clusters. They also usually optimize their objectives based on fixed graph similarity matrices of all views. In this paper, we propose a general Graph-based Multi-view Clustering (GMC) to tackle these problems. GMC takes the data graph matrices of all views and fuses them to generate a unified matrix. The unified matrix in turn improves the data graph matrix of each view, and also gives the final clusters directly. The key novelty of GMC is its learning method, which can help the learning of each view graph matrix and the learning of the unified matrix in a mutual reinforcement manner. A novel multi-view fusion technique can automatically weight each data graph matrix to derive the unified matrix. A rank constraint without introducing a tuning parameter is also imposed on the Laplacian matrix of the unified matrix, which helps partition the data points naturally into the required number of clusters. An alternating iterative optimization algorithm is presented to optimize the objective function. Experimental results demonstrate that the proposed method outperforms state-of-the-art baselines markedly.ques.
第一步: 交代研究背景	Multi-view graph-based clustering aims to provide clustering solutions to multi-view data.
第二步: 概括当前方法
第三步: 一般介绍现有方法的不足，论文给出的一些解决办法。	However, most existing methods do not give sufficient consideration to weights of different views and require an additional clustering step to produce the final clusters. They also usually optimize their objectives based on fixed graph similarity matrices of all views.
第四步: 提出当前的方法	In this paper, we propose a general Graph-based Multi-view Clustering (GMC) to tackle these problems.
第五步: 在提出论文的方法之后，需要进行对自己提出的方法的大致的介绍	GMC takes the data graph matrices of all views and fuses them to generate a unified matrix. The unified matrix in turn improves the data graph matrix of each view, and also gives the final clusters directly. The key novelty of GMC is its learning method, which can help the learning of each view graph matrix and the learning of the unified matrix in a mutual reinforcement manner. A novel multi-view fusion technique can automatically weight each data graph matrix to derive the unified matrix. A rank constraint without introducing a tuning parameter is also imposed on the Laplacian matrix of the unified matrix, which helps partition the data points naturally into the required number of clusters.
第六步: 第五步进行了理论上的阐述。这一步呢，通常是对提出的算法怎么样实现优化的一句话或者两句话。不能太长，因为有字数限制。（可有，也可以没有，视具体论文而定）	An alternating iterative optimization algorithm is presented to optimize the objective function.
第七步: 简要介绍一下实验，这个比较的套路，一般都是这个套路。	Experimental results demonstrate that the proposed method outperforms state-of-the-art baselines markedly.ques.

摘要解读

第一步: 交代背景：多视角数据的普遍性和重要性

第二步: 概括当前方法 。

第三步: 一般介绍现有方法的不足 

第四步: 提出当前的方法

第五步: 在提出论文的方法之后，需要进行对自己提出的方法的大致的介绍 

第六步: 第五步进行了理论上的阐述。这一步呢，通常是对提出的算法怎么样实现优化的一句话或者两句话。不能太长，因为有字数限制。

第七步: 简要介绍一下实验，这个比较的套路。

以上就是大致的一个流程，我也正在学习，若有不足请各位耐心支出。非常感谢。

一般的摘要都会遵循这七个步骤，不同的步骤之间可能会融合到一块进行书写，在我们自己进行书写摘要的时候，可以参照这个步骤。如果自己在某个步骤实在想不出来，就暂时空下来。