Large-Scale Clustering Through Functional Embedding
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.
Cite
Text
Ratle et al. "Large-Scale Clustering Through Functional Embedding." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2008. doi:10.1007/978-3-540-87481-2_18Markdown
[Ratle et al. "Large-Scale Clustering Through Functional Embedding." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2008.](https://mlanthology.org/ecmlpkdd/2008/ratle2008ecmlpkdd-largescale/) doi:10.1007/978-3-540-87481-2_18BibTeX
@inproceedings{ratle2008ecmlpkdd-largescale,
title = {{Large-Scale Clustering Through Functional Embedding}},
author = {Ratle, Frédéric and Weston, Jason and Miller, Matthew L.},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2008},
pages = {266-281},
doi = {10.1007/978-3-540-87481-2_18},
url = {https://mlanthology.org/ecmlpkdd/2008/ratle2008ecmlpkdd-largescale/}
}