An algorithm to compare two-dimensional footwear outsole images using maximum cliques and speeded-up robust feature [0.03%]
基于最大团和加速稳健特征的鞋印-outsole图像匹配算法研究
Soyoung Park,Alicia Carriquiry
Soyoung Park
Footwear examiners are tasked with comparing an outsole impression (Q) left at a crime scene with an impression (K) from a database or from the suspect's shoe. We propose a method for comparing two shoe outsole impressions that relies on ro...
Nonlinear variable selection with continuous outcome: a fully nonparametric incremental forward stagewise approach [0.03%]
非线性变量选择：具有连续结果的完全非参数增量向前分段方法
Tianwei Yu
Tianwei Yu
We present a method of variable selection for the sparse generalized additive model. The method doesn't assume any specific functional form, and can select from a large number of candidates. It takes the form of incremental forward stagewis...
Eugene Demidenko
Eugene Demidenko
Typically, when referring to a model-based classification, the mixture distribution approach is understood. In contrast, we revive the hard-classification model-based approach developed by Banfield and Raftery (1993) for which K-means is eq...
Whole-Volume Clustering of Time Series Data from Zebrafish Brain Calcium Images via Mixture Modeling [0.03%]
基于混合模型的小鼠脑钙成像时间序列整体聚类方法
Hien D Nguyen,Jeremy F P Ullmann,Geoffrey J McLachlan et al.
Hien D Nguyen et al.
Calcium is a ubiquitous messenger in neural signaling events. An increasing number of techniques are enabling visualization of neurological activity in animal models via luminescent proteins that bind to calcium ions. These techniques gener...
Fei Tang,Hemant Ishwaran
Fei Tang
Random forest (RF) missing data algorithms are an attractive approach for imputing missing data. They have the desirable properties of being able to handle mixed types of missing data, they are adaptive to interactions and nonlinearity, and...
Nonlinear Joint Latent Variable Models and Integrative Tumor Subtype Discovery [0.03%]
非线性联合潜在变量模型与整合肿瘤亚型发现
Binghui Liu,Xiaotong Shen,Wei Pan
Binghui Liu
Integrative analysis has been used to identify clusters by integrating data of disparate types, such as deoxyribonucleic acid (DNA) copy number alterations and DNA methylation changes for discovering novel subtypes of tumors. Most existing ...
Hierarchical Models for Multiple, Rare Outcomes Using Massive Observational Healthcare Databases [0.03%]
基于大规模观察性医疗数据库的多发罕见结果的层次模型研究
Trevor R Shaddox,Patrick B Ryan,Martijn J Schuemie et al.
Trevor R Shaddox et al.
Clinical trials often lack power to identify rare adverse drug events (ADEs) and therefore cannot address the threat rare ADEs pose, motivating the need for new ADE detection techniques. Emerging national patient claims and electronic healt...
Adrian E Raftery
Adrian E Raftery
Probabilistic forecasts are becoming more and more available. How should they be used and communicated? What are the obstacles to their use in practice? I review experience with five problems where probabilistic forecasting played an import...
Turbo-SMT: Parallel Coupled Sparse Matrix-Tensor Factorizations and Applications [0.03%]
加速SMT算法：并行耦合的稀疏矩阵张量因式分解及应用
Evangelos E Papalexakis,Christos Faloutsos,Tom M Mitchell et al.
Evangelos E Papalexakis et al.
How can we correlate the neural activity in the human brain as it responds to typed words, with properties of these terms (like 'edible', 'fits in hand')? In short, we want to find latent variables, that jointly explain both the brain activ...
Composite large margin classifiers with latent subclasses for heterogeneous biomedical data [0.03%]
用于异构生物医学数据的潜在子类复合大边缘分类器
Guanhua Chen,Yufeng Liu,Dinggang Shen et al.
Guanhua Chen et al.
High dimensional classification problems are prevalent in a wide range of modern scientific applications. Despite a large number of candidate classification techniques available to use, practitioners often face a dilemma of choosing between...