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首页> 外文期刊>Pattern Analysis and Machine Intelligence, IEEE Transactions on >Exploiting Unsupervised and Supervised Constraints for Subspace Clustering
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Exploiting Unsupervised and Supervised Constraints for Subspace Clustering

机译：利用子空间聚类的无监督约束和受监督约束

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摘要

Data in many image and video analysis tasks can be viewed as points drawn from multiple low-dimensional subspaces with each subspace corresponding to one category or class. One basic task for processing such kind of data is to separate the points according to the underlying subspace, referred to as subspace clustering. Extensive studies have been made on this subject, and nearly all of them use , meaning the points can be drawn from everywhere of a subspace, to represent the data. In this paper, we attempt to do subspace clustering based on a that the data is further restricted in the corresponding subspaces, e.g., belonging to a submanifold or satisfying the spatial regularity constraint. This assumption usually describes the real data better, such as differently moving objects in a video scene and face images of different subjects under varying illumination. A unified integer linear programming optimization framework is used to approach subspace clustering, which can be efficiently solved by a branch-and-bound (BB) method. We also show that various kinds of supervised information, such as subspace number, outlier ratio, pairwise constraints, size prior and etc., can be conveniently incorporated into the proposed framework. Experiments on real data show that the proposed method outperforms the state-of-the-art algorithms significantly in clustering accuracy. The effectiveness of the proposed method in exploiting supervised information is also demonstrated.
机译：可以将许多图像和视频分析任务中的数据视为从多个低维子空间中提取的点，每个子空间对应一个类别或类别。处理此类数据的一项基本任务是根据基础子空间将点分开，这称为子空间聚类。已经对该主题进行了广泛的研究，几乎所有的研究都使用，这意味着可以从子空间的任何地方绘制点来表示数据。在本文中，我们尝试基于以下方法进行子空间聚类：将数据进一步限制在相应的子空间中，例如，属于一个子流形或满足空间规则性约束。这种假设通常可以更好地描述真实数据，例如视频场景中不同的移动对象以及光照变化下不同主体的面部图像。统一整数线性规划优化框架用于子空间聚类，可以通过分支定界（BB）方法有效解决。我们还表明，可以将各种监督信息（例如子空间数量，异常值比率，成对约束，大小先验等）方便地合并到建议的框架中。在真实数据上的实验表明，该方法在聚类精度方面明显优于最新算法。还证明了该方法在利用监督信息中的有效性。

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