资源分类
》 资源 》论文》FastEx: Hash Clustering with Exponential Families

FastEx: Hash Clustering with Exponential Families

2020-01-13 | |  72 |   36 |   0

Abstract

Clustering is a key component in any data analysis toolbox. Despite its importance, scalable algorithms often eschew rich statistical models in favor of simpler descriptions such as k-means clustering. In this paper we present a sampler, capable of estimating mixtures of exponential families. At its heart lies a novel proposal distribution using random projections to achieve high throughput in generating proposals, which is crucial for clustering models with large numbers of clusters.

上一篇：Scalable Inference of Overlapping Communities

下一篇：Provable ICA with Unknown Gaussian Noise, withImplications for Gaussian Mixtures and Autoencoders

用户评价
全部评价

热门资源

  • The Variational S...

    Unlike traditional images which do not offer in...

  • Learning to Predi...

    Much of model-based reinforcement learning invo...

  • Stratified Strate...

    In this paper we introduce Stratified Strategy ...

  • Learning to learn...

    The move from hand-designed features to learned...

  • A Mathematical Mo...

    Direct democracy, where each voter casts one vo...