QQ客服 官方微博 反馈留言

	Spectral Clustering of Large-scale Data by Directly Solving Normalized Cut
	Xiaojun Chen; Weijun Hong; Feiping Nie; Dan He; Min Yang; Joshua Z. Huang
	2018
会议日期	2018
会议地点	英国伦敦
英文摘要	During the past decades, many spectral clustering algorithms have been proposed. However, their high computational com- plexities hinder their applications on large-scale data. More- over, most of them use a two-step approach to obtain the optimal solution, which may deviate from the solution by di- rectly solving the original problem. In this paper, we propose a new optimization algorithm, namely Direct Normalized Cut (DNC), to directly optimize the normalized cut model. To cope with large-scale data, a Fast Normalized Cut (FNC) method with linear time and space complexities is proposed by extending DNC with a anchor-based strategy. In the new method, we first seek a set of anchors and then construct a representative similarity matrix by computing distances between the anchors and the whole data set. To find high quality anchors that best represent the whole data set, we propose a Balanced k-means (BKM) to partition a data set into balanced clusters and use the cluster centers as anchors. Then DNC is used to obtain the final cluster result from the representative similarity matrix. A series of experiments were conducted on both synthetic data and real-world data sets, and the experimental results show the superior perfor- mance of BKM, DNC and FNC.
语种	英语
内容类型	会议论文
源URL	[http://ir.siat.ac.cn:8080/handle/172644/14103]
专题	深圳先进技术研究院_数字所
推荐引用方式 GB/T 7714	Xiaojun Chen,Weijun Hong,Feiping Nie,et al. Spectral Clustering of Large-scale Data by Directly Solving Normalized Cut[C]. 见:. 英国伦敦. 2018.

个性服务

查看访问统计

相关权益政策

暂无数据

收藏/分享

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。