Frontiers in Artificial Intelligence and Applications
Chen (2018) proposed a scalable spectral clustering algorithm for cosine similarity to handle the task of clustering large data sets. It runs extremely fast, with a linear complexity in the size of the data, and achieves state of the art accuracy. This paper conducts perturbation analysis of the algorithm to understand the effect of discarding a perturbation term in an eigendecomposition step. Our results show that the accuracy of the approximation by the scalable algorithm depends on the connectivity of the clusters, their separation and sizes, and is especially accurate for large data sets.
Cosine similarity, Perturbation analysis, Spectral clustering
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This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License
Mathematics and Statistics
Guangliang Chen. "The larger the better: Analysis of a scalable spectral clustering algorithm with cosine similarity" Frontiers in Artificial Intelligence and Applications (2021): 488-495. https://doi.org/10.3233/FAIA210280