Sampled Weighted Min-Hashing for Large-Scale Topic Mining
Abstract
We present Sampled Weighted Min-Hashing (SWMH), a randomized approach to automatically mine topics from large-scale corpora. SWMH generates multiple random partitions of the corpus vocabulary based on term co-occurrence and agglomerates highly overlapping inter-partition cells to produce the mined topics. While other approaches define a topic as a probabilistic distribution over a vocabulary, SWMH topics are ordered subsets of such vocabulary. Interestingly, the topics mined by SWMH underlie themes from the corpus at different levels of granularity. We extensively evaluate the meaningfulness of the mined topics both qualitatively and quantitatively on the NIPS (1.7 K documents), 20 Newsgroups (20 K), Reuters (800 K) and Wikipedia (4 M) corpora. Additionally, we compare the quality of SWMH with Online LDA topics for document representation in classification.
Keywords
Cite
@article{arxiv.1509.01771,
title = {Sampled Weighted Min-Hashing for Large-Scale Topic Mining},
author = {Gibran Fuentes-Pineda and Ivan Vladimir Meza-Ruiz},
journal= {arXiv preprint arXiv:1509.01771},
year = {2015}
}
Comments
10 pages, Proceedings of the Mexican Conference on Pattern Recognition 2015