Abstract
This paper proposes a novel clustering approach called Scalable Nonnegative Spectral Clustering (SNSC). Specifically, SNSC preserves the original nonnegative characteristic of the indicator matrix, which leads to a more tractable optimization problem with an accurate solution. Due to the nonnegativity, SNSC offers high interpretability to the indicator matrix, that is, the final cluster labels can be directly obtained without post-processing. SNSC also scales linearly with the data size, thus it can be easily applied to large-scale problems. In addition, limited label information can be naturally incorporated into SNSC for improving clustering performance. Extensive experiments demonstrate the superiority of SNSC as compared to the state-of-the-art methods.
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Acknowledgments
The paper is supported by the National Key R&D Program (2016YFB1000101), the National Natural Science Foundation of China (11801595, U1811462), the Natural Science Foundation of Guangdong (2018A030310076), the Program for Guangdong Introducing Innovative and Entrepreneurial Teams (2016ZT06D211) and the CCF Opening Project of Information System.
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Hu, W., Chen, C., Ye, F., Zheng, Z., Ling, G. (2019). Nonnegative Spectral Clustering for Large-Scale Semi-supervised Learning. In: Li, G., Yang, J., Gama, J., Natwichai, J., Tong, Y. (eds) Database Systems for Advanced Applications. DASFAA 2019. Lecture Notes in Computer Science(), vol 11448. Springer, Cham. https://doi.org/10.1007/978-3-030-18590-9_30
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DOI: https://doi.org/10.1007/978-3-030-18590-9_30
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