Results 31 to 40 of about 1,042,888 (328)
Methodology, 2022 Researchers often examine whether two continuous variables (X and Y) are linearly related. Pearson’s correlation (r) is a widely-employed statistic for assessing bivariate linearity.
Johnson Ching-Hong Li
doaj +1 more source
Robust Orthogonal Complement Principal Component Analysis [PDF]
, 2016 Recently, the robustification of principal component analysis has attracted lots of attention from statisticians, engineers and computer scientists. In this work we study the type of outliers that are not necessarily apparent in the original observation ...
Li, Shijie, She, Yiyuan, Wu, Dapeng
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Graph Neural Networks for Modeling Causality in International Trade
Proceedings of the International Florida Artificial Intelligence Research Society Conference, 2021 Neural network algorithms have proven successful for accurate classifications in many domains such as image recognition and semantic parsing. However, they have long suffered from the lack of ability to measure causality, predict outliers effectively, or
Anderson Monken +4 more
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2-D iteratively reweighted least squares lattice algorithm and its application to defect detection in textured images [PDF]
, 2006 In this paper, a 2-D iteratively reweighted least squares lattice algorithm, which is robust to the outliers, is introduced and is applied to defect detection problem in textured images.
Ercil, Aytul +7 more
core +2 more sources
Practical Bayesian optimization in the presence of outliers [PDF]
, 2017 Inference in the presence of outliers is an important field of research as outliers are ubiquitous and may arise across a variety of problems and domains. Bayesian optimization is method that heavily relies on probabilistic inference.
Martinez-Cantin, Ruben +2 more
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Contextual Outlier Interpretation
, 2018 Outlier detection plays an essential role in many data-driven applications to identify isolated instances that are different from the majority. While many statistical learning and data mining techniques have been used for developing more effective ...
Hu, Xia, Liu, Ninghao, Shin, Donghwa
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Robust hierarchical k-center clustering [PDF]
, 2015 One of the most popular and widely used methods for data clustering is hierarchical clustering. This clustering technique has proved useful to reveal interesting structure in the data in several applications ranging from computational biology to computer
Lattanzi, Silvio +3 more
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Molecular Oncology, EarlyView.Development of therapies targeting cancer‐associated fibroblasts (CAFs) necessitates preclinical model systems that faithfully represent CAF–tumor biology. We established an in vitro coculture system of patient‐derived pancreatic CAFs and tumor cell lines and demonstrated its recapitulation of primary CAF–tumor biology with single‐cell transcriptomics ...
Elysia Saputra +10 more
wiley +1 more source
A Feature-Cascaded Correntropy LSTM for Tourists Prediction
IEEE Access, 2021 Forecasting the number of tourists is significant to public safety, which can enable the government to control the sudden influx of tourists timely. The temporal dependence (closeness and period), external factors such as holidays, government policy, as ...
Yuehai Chen +4 more
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Robustness to outliers in location-scale parameter model using log-regularly varying distributions [PDF]
, 2015 Estimating the location and scale parameters is common in statistics, using, for instance, the well-known sample mean and standard deviation. However, inference can be contaminated by the presence of outliers if modeling is done with light-tailed ...
Desgagné, Alain
core +2 more sources