.png){kind=icon}
返回
Provable sparse tensor decomposition
.png){kind=icon}
OA
AI
DOI：10.1111/rssb.12190.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
We propose a novel sparse tensor decomposition method, namely the tensor truncated power method, that incorporates variable selection in the estimation of decomposition components. The sparsity is achieved via an efficient truncation step embedded in the tensor power iteration. Our method applies to a broad family of high dimensional latent variable models, including high dimensional Gaussian mixtures and mixtures of sparse regressions. A thorough theoretical investigation is further conducted. In particular, we show that the final decomposition estimator is guaranteed to achieve a local statistical rate, and we further strengthen it to the global statistical rate by introducing a proper initialization procedure. In high dimensional regimes, the statistical rate obtained significantly improves those shown in the existing non-sparse decomposition methods. The empirical advantages of tensor truncated power are confirmed in extensive simulation results and two real applications of click-through rate prediction and high dimensional gene clustering.
Keyword:
Global convergence
Latent variable models
Non-convex optimization
Sparsity
Tensor decomposition
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
J
IF:
3.6
论文数:
1.5K
被引数:
3.2W
机构
引用论文
Prevalent and persistent viral infection in cultures of the coral algal endosymbiont Symbiodinium.png)
Coral Reefs
Coral ReefsIF0
PREAI
分享收藏DC Offset Error Compensation Algorithm for PR Current Control of a Single-Phase Grid-Tied InverterEnergies
OAAI
EnergiesIF0
OAAI分享收藏Association of Subjective Global Assessment with outcomes in the intensive care unit: A retrospective cohort study主观全球评估与重症监护病房结局的关系：一项回顾性队列研究OAAI
OAAI分享收藏
查看更多内容