Data-driven research on chemical features of Jingdezhen and Longquan celadon by energy dispersive X-ray fluorescence

He, Ziyang; Zhang, Maolin; Zhang, Haozhe

doi:10.1016/j.ceramint.2015.12.030

Cited by 21 publications

(9 citation statements)

References 8 publications

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“…Although RF methodology has proven itself to be a reliable predictive approach in many application areas [3] [10], there are some cases where random forests may suffer. First, as a fully nonparametric predictive algorithm, random forests may not efficiently incorporate known relationships between the response and the predictors.…”

Section: Introductionmentioning

confidence: 99%

Regression-Enhanced Random Forests

Zhang¹,

Nettleton²,

Zhu³

2019

Preprint

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Random forest (RF) methodology is one of the most popular machine learning techniques for prediction problems. In this article, we discuss some cases where random forests may suffer and propose a novel generalized RF method, namely regression-enhanced random forests (RERFs), that can improve on RFs by borrowing the strength of penalized parametric regression. The algorithm for constructing RERFs and selecting its tuning parameters is described. Both simulation study and real data examples show that RERFs have better predictive performance than RFs in important situations often encountered in practice. Moreover, RERFs may incorporate known relationships between the response and the predictors, and may give reliable predictions in extrapolation problems where predictions are required at points out of the domain of the training dataset. Strategies analogous to those described here can be used to improve other machine learning methods via combination with penalized parametric regression techniques.

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Section: Introductionmentioning

confidence: 99%

Regression-Enhanced Random Forests

Zhang¹,

Nettleton²,

Zhu³

2019

Preprint

Self Cite

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“…Further, archaelogical excavations [44] indicate that Jingdezhen soils have lower Fe 2 O 3 and TiO 2 content and higher silica (CaO) than soils at the Dayao sites. As in [43], our display also indicates distinctiveness of genuine Longquan glaze from its imitation, but there is improvement over time, so much so that some later Longquan samples (Qing period) are close in composition to the Jingdezhen samples. Genuine Longquan chemical composition itself evolved over the four dynasties, as indicated in the figure.…”

Section: Compositional Datasetsmentioning

confidence: 51%

Fully Three-dimensional Radial Visualization

Zhu,

Dai,

Maitra

2021

Preprint

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We develop methodology for three-dimensional (3D) radial visualization (RadViz) of multidimensional datasets. The classical two-dimensional (2D) RadViz visualizes multivariate data in the 2D plane by mapping every observation to a point inside the unit circle. Our tool, RadViz3D, distributes anchor points uniformly on the 3D unit sphere. We show that this uniform distribution provides the best visualization with minimal artificial visual correlation for data with uncorrelated variables. However, anchor points can be placed exactly equi-distant from each other only for the five Platonic solids, so we provide equi-distant anchor points for these five settings, and approximately equi-distant anchor points via a Fibonacci grid for the other cases. Our methodology, implemented in the R package radviz3d, makes fully 3D RadViz possible and is shown to improve the ability of this nonlinear technique in more faithfully displaying simulated data as well as the crabs, olive oils and wine datasets. Additionally, because radial visualization is naturally suited for compositional data, we use RadViz3D to illustrate (i) the chemical composition of Longquan celadon ceramics and their Jingdezhen imitation over centuries, and (ii) US regional SARS-Cov-2 variants' prevalence in the Covid-19 pandemic during the summer 2021 surge of the Delta variant.

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“…In particular, assumptions and findings of Corollary 4.1 hold well in real-world data sets, especially, when the data sets include densely connected clusters of points, e.g. Ceramic [57] and Iris [55]. 4 Additionally, corrupted data sets, e.g.…”

Section: B Outlier Effects On Target Vector Vmentioning

confidence: 81%

Fast and Robust Sparsity-Aware Block Diagonal Representation

Taştan,

Muma,

Zoubir

2024

IEEE Trans. Signal Process.

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The block diagonal structure of an affinity matrix is a commonly desired property in cluster analysis because it represents clusters of feature vectors by non-zero coefficients that are concentrated in blocks. However, recovering a block diagonal affinity matrix is challenging in real-world applications, in which the data may be subject to outliers and heavy-tailed noise that obscure the hidden cluster structure. To address this issue, we first analyze the effect of different fundamental outlier types in graph-based cluster analysis. A key idea that simplifies the analysis is to introduce a vector that represents a block diagonal matrix as a piece-wise linear function of the similarity coefficients that form the affinity matrix. We reformulate the problem as a robust piece-wise linear fitting problem and propose a Fast and Robust Sparsity-Aware Block Diagonal Representation (FRS-BDR) method, which jointly estimates cluster memberships and the number of blocks. Comprehensive experiments on a variety of real-world applications demonstrate the effectiveness of FRS-BDR in terms of clustering accuracy, robustness against corrupted features, computation time and cluster enumeration performance.

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Data-driven research on chemical features of Jingdezhen and Longquan celadon by energy dispersive X-ray fluorescence

Cited by 21 publications

References 8 publications

Regression-Enhanced Random Forests

Regression-Enhanced Random Forests

Fully Three-dimensional Radial Visualization

Fast and Robust Sparsity-Aware Block Diagonal Representation

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