Note on the Equivalence of Orthogonalizing EM and Proximal Gradient Descent [0.03%]
关于正交化EM和proximal梯度下降等价性的一个注记
James Yang,Trevor Hastie
James Yang
Xiong et al. (2016) develop a method called orthogonalizing EM (OEM) to solve penalized regression problems for tall data. While OEM is developed in the context of the EM algorithm, we show that it is, in fact, an instance of proximal gradi...
Wesley Lee,Tyler H McCormick,Joshua Neil et al.
Wesley Lee et al.
We develop a real-time anomaly detection method for directed activity on large, sparse networks. We model the propensity for future activity using a dynamic logistic model with interaction terms for sender- and receiver-specific latent fact...
Robust Low-rank Tensor Decomposition with the [Formula: see text] Criterion [0.03%]
基于[公式: 见文本]准则的稳健低秩张量分解
Qiang Heng,Eric C Chi,Yufeng Liu
Qiang Heng
The growing prevalence of tensor data, or multiway arrays, in science and engineering applications motivates the need for tensor decompositions that are robust against outliers. In this paper, we present a robust Tucker decomposition estima...
Xiaoqian Liu,Eric C Chi,Kenneth Lange
Xiaoqian Liu
Building on previous research of Chi and Chi (2022), the current paper revisits estimation in robust structured regression under the L2E criterion. We adopt the majorization-minimization (MM) principle to design a new algorithm for updating...
Xinyu Zhao,Jiuyun Hu,Yajun Mei et al.
Xinyu Zhao et al.
High-dimensional data has become popular due to the easy accessibility of sensors in modern industrial applications. However, one specific challenge is that it is often not easy to obtain complete measurements due to limited sensing powers ...
Bandit Change-Point Detection for Real-Time Monitoring High-Dimensional Data Under Sampling Control [0.03%]
带实时采样控制的变点检测算法研究
Wanrong Zhang,Yajun Mei
Wanrong Zhang
In many real-world problems of real-time monitoring high-dimensional streaming data, one wants to detect an undesired event or change quickly once it occurs, but under the sampling control constraint in the sense that one might be able to o...
Robert Tibshirani,Xiaotong Suo
Robert Tibshirani
We consider regression scenarios where it is natural to impose an order constraint on the coefficients. We propose an order-constrained version of ℓ 1-regularized regression (Lasso) for this problem, and show how to solve it efficiently us...
Guan Yu,Haoda Fu,Yufeng Liu
Guan Yu
Budget constraints become an important consideration in modern predictive modeling due to the high cost of collecting certain predictors. This motivates us to develop cost-constrained predictive modeling methods. In this paper, we study a n...
Trevor Hastie
Trevor Hastie
Ridge or more formally ℓ 2 regularization shows up in many areas of statistics and machine learning. It is one of those essential devices that any good data scientist needs to master for their craft. In this brief ridge fest, I have collec...
Model-Based Clustering of Nonparametric Weighted Networks with Application to Water Pollution Analysis [0.03%]
基于模型的非参加权网络聚类及其在水质分析中的应用
Amal Agarwal,Lingzhou Xue
Amal Agarwal
Water pollution is a major global environmental problem, and it poses a great environmental risk to public health and biological diversity. This work is motivated by assessing the potential environmental threat of coal mining through increa...