Spectral Clustering of Large-scale Data by Directly Solving Normalized Cut
文献类型：会议论文
| 作者 | Xiaojun Chen; Weijun Hong; Feiping Nie; Dan He; Min Yang; Joshua Z. Huang |
| 出版日期 | 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 |
| 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. |
入库方式： OAI收割
来源：深圳先进技术研究院
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