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Large-Scale Clustering through Functional Embedding
This video was recorded at European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), Antwerp 2008. 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.
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This work is licensed under a Attribution-NonCommercial-NoDerivs 3.0 United States
