Approximate spectral clustering with eigenvector selection and self-tuned $k$
This work addresses a practical limitation in clustering for applications with large datasets, though it is incremental as it builds on existing approximate spectral clustering techniques.
The paper tackles the problem of approximate spectral clustering requiring a known number of clusters by proposing an algorithm that automatically estimates k using relevance metrics and a growing neural gas approximation, achieving competitive performance with manual methods.
The recently emerged spectral clustering surpasses conventional clustering methods by detecting clusters of any shape without the convexity assumption. Unfortunately, with a computational complexity of $O(n^3)$, it was infeasible for multiple real applications, where $n$ could be large. This stimulates researchers to propose the approximate spectral clustering (ASC). However, most of ASC methods assumed that the number of clusters $k$ was known. In practice, manual setting of $k$ could be subjective or time consuming. The proposed algorithm has two relevance metrics for estimating $k$ in two vital steps of ASC. One for selecting the eigenvectors spanning the embedding space, and the other to discover the number of clusters in that space. The algorithm used a growing neural gas (GNG) approximation, GNG is superior in preserving input data topology. The experimental setup demonstrates the efficiency of the proposed algorithm and its ability to compete with similar methods where $k$ was set manually.