Landmark-Based Spectral Clustering with Local Similarity Representation
Clustering analysis is one of the most important tasks in statistics, machine learning, and image processing. Compared to those clustering methods based on Euclidean geometry, spectral clustering has no limitations on the shape of data and can detect linearly non-separable pattern. Due to the high computation complexity of spectral clustering, it is difficult to handle large-scale data sets. Recently, several methods have been proposed to accelerate spectral clustering. Among these methods, landmark-based spectral clustering is one of the most direct methods without losing much information embedded in the data sets. Unfortunately, the existing landmark-based spectral clustering methods do not utilize the prior knowledge embedded in a given similarity function. To address the aforementioned challenges, a landmark-based spectral clustering method with local similarity representation is proposed. The proposed method firstly encodes the original data points with their most ‘similar’ landmarks by using a given similarity function. Then the proposed method performs singular value decomposition on the encoded data points to get the spectral embedded data points. Finally run k-means on the embedded data points to get the clustering results. Extensive experiments show the effectiveness and efficiency of the proposed method.
KeywordsLandmark representation Spectral clustering Clustering analysis
The authors would like to thank the financial support of National Natural Science Foundation of China (Project NO. 61672528, 61403405, 61232016, 61170287).
- 2.Liu, X., Zhou, S., Wang, Y., Li, M., Dou, Y., Zhu, E., Yin, J.: Optimal neighborhood kernel clustering with multiple kernels. In: Proceedings of the 31st Conference on Artificial Intelligence, pp. 2266–2272 (2017)Google Scholar
- 3.Wu, Y., Zhang, B., Yi, X., Tang, Y.: Communication-motion planning for wireless relay-assisted multi-robot system. IEEE Wirel. Commun. Lett. 5(6), 568–571 (2016)Google Scholar
- 5.Li, Z., Chen, J.: Superpixel segmentation using linear spectral clustering. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1356–1363 (2015)Google Scholar
- 8.Yan, D., Huang, L., Jordan, M.I.: Fast approximate spectral clustering. In: International Conference on Knowledge Discovery and Data Mining, pp. 1–23 (2009)Google Scholar
- 9.Chen, X.: Large scale spectral clustering with landmark-based representation. In: Proceedings of the Twenty-Fifth AAAI Conference on Artificial Intelligence Large, number Chung 1997, pp. 313–318 (2011)Google Scholar
- 10.Wang, J., Yang, J., Yu, K., Lv, F., Huang, T.: Locality-constrained linear coding for image classification. In: Computer Vision and Pattern Recognition, pp. 27–30 (2014)Google Scholar
- 11.Wang, M., Fu, W., Hao, S., Tao, D., Wu, X.: Scalable semi-supervised learning by efficient anchor graph regularization. IEEE Trans. Knowl. Data Eng. 28(7), 1864–1877 (2016)Google Scholar
- 12.Liu, W., He, J., Chang, S.-F.: Large graph construction for scalable semi-supervised learning. In: Proceedings of the 25th International Conference on Machine Learning, pp. 679–689 (2010)Google Scholar
- 13.Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12, 2825–2830 (2011)MathSciNetMATHGoogle Scholar