تقرير
Sketched Clustering via Hybrid Approximate Message Passing
| العنوان: | Sketched Clustering via Hybrid Approximate Message Passing |
|---|---|
| المؤلفون: | Byrne, Evan, Chatalic, Antoine, Gribonval, Remi, Schniter, Philip |
| سنة النشر: | 2017 |
| المجموعة: | Computer Science Mathematics |
| مصطلحات موضوعية: | Computer Science - Information Theory |
| الوصف: | In sketched clustering, a dataset of $T$ samples is first sketched down to a vector of modest size, from which the centroids are subsequently extracted. Advantages include i) reduced storage complexity and ii) centroid extraction complexity independent of $T$. For the sketching methodology recently proposed by Keriven, et al., which can be interpreted as a random sampling of the empirical characteristic function, we propose a sketched clustering algorithm based on approximate message passing. Numerical experiments suggest that our approach is more efficient than the state-of-the-art sketched clustering algorithm "CL-OMPR" (in both computational and sample complexity) and more efficient than k-means++ when $T$ is large. |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/1712.02849 |
| رقم الأكسشن: | edsarx.1712.02849 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |