Large-Scale Clustering through Functional Embedding

Abstract

Abstract. We present a new framework for large-scale data clustering. The main idea is to modify functional dimensionality reduction techniques to directly optimize over discrete labels using stochastic gradient descent. Compared to methods like spectral clustering our approach solves a single optimization problem, rather than an ad-hoc two-stage optimization approach, does not require a matrix inversion, can easily encode prior knowledge in the set of implementable functions, and does not have an "out-of-sample" problem. Experimental results on both artificial and real-world datasets show the usefulness of our approach.

Machine learning in complex networks: modeling, analysis, and applications

“…For the stochastic methods, such as the particle competition method, thirty independent runs were performed and the corresponding mean is provided. from (Ratle et al, 2008) and (Liu et al, 2010). For more information about the parameters used in the competing techniques, see the aforementioned references.…”