The Alternating Direction Method of Multipliers for Sufficient Dimension Reduction by Sheng Ma, Qin Jiang, Zaiqiang Ku

“…The minimum average variance estimation (MAVE) method has proven to be an effective approach to sufficient dimension reduction. In this study, we apply the computationally efficient optimization algorithm named alternating direction method of multipliers (ADMM) to a particular approach (MAVE or minimum average variance estimation) to the problem of sufficient dimension reduction (SDR). …”
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Dimension reduction for maximum matchings and the Fastest Mixing Markov Chain by Jain, Vishesh, Pham, Huy, Vuong, Thuy-Duong

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Remarks on homogenization and $3D$-$2D$ dimension reduction of unbounded energies on thin films by Anza Hafsa, Omar, Mandallena, Jean-Philippe

“…We study periodic homogenization and $3D$-$2D$ dimension reduction by $\Gamma (\pi )$-con-vergence of heterogeneous thin films whose the stored-energy densities have no polynomial growth. …”
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A Manifold-Based Dimension Reduction Algorithm Framework for Noisy Data Using Graph Sampling and Spectral Graph by Tao Yang, Dongmei Fu, Jintao Meng

“…This paper proposes a new manifold-based dimension reduction algorithm framework. It can deal with the dimension reduction problem of data with noise and give the dimension reduction results with the deviation values caused by noise interference. …”
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Multidimensional Scaling-Based Data Dimension Reduction Method for Application in Short-Term Traffic Flow Prediction for Urban Road Network by Yi Zhao, Satish V. Ukkusuri, Jian Lu

“…The data dimension reduction method can be divided into three steps. …”
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Geometrical shape learning as basis for compact microstructure representations and microstructure-properties linkages by Rodrigo Iza Teran, Daniela Steffes-lai, Lukas Morand

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Mathematical analysis and dynamic active subspaces for a long term model of HIV by Tyson Loudon, Stephen Pankavich

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Seurat function argument values in scRNA-seq data analysis: potential pitfalls and refinements for biological interpretation by Mikhail Arbatsky, Ekaterina Vasilyeva, Veronika Sysoeva, Ekaterina Semina, Ekaterina Semina, Valeri Saveliev, Kseniya Rubina

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Multivariate Analyses with Two-Step Dimension Reduction for an Association Study Between ¹¹C-Pittsburgh Compound B and Magnetic Resonance Imaging in Alzheimer’s Disease by Atsushi Kawaguchi, Fumio Yamashita

“…To study the relationship between Aβ deposition and brain structure, as determined using ¹¹C-Pittsburgh compound B (PiB) and magnetic resonance imaging (MRI), respectively, we developed a regression model with PiB and MRI data as the predictor and response variables, respectively, and proposed a regression method for studying the association between them based on a supervised sparse multivariate analysis with dimension reduction based on a composite paired basis function. …”
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MiMeJF: Application of Coupled Matrix and Tensor Factorization (CMTF) for Enhanced Microbiome-Metabolome Multi-Omic Analysis by Zheyuan Ou, Xi Fu, Dan Norbäck, Ruqin Lin, Jikai Wen, Yu Sun

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Estimating the visibility in foggy weather based on meteorological and video data: A Recurrent Neural Network approach by Jian Chen, Ming Yan, Muhammad Rabea Hanzla Qureshi, Keke Geng

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Asymptotic modeling of viscoelastic thin plates and slender beams, a unifying approach by Terapabkajornded, Yotsawat, Orankitjaroen, Somsak, Licht, Christian, Weller, Thibaut

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An Empirical Configuration Study of a Common Document Clustering Pipeline by Anton Eklund, Mona Forsman, Frank Drewes

“…In this paper, we study document clustering with the common clustering pipeline that includes vectorization with BERT or Doc2Vec, dimension reduction with PCA or UMAP, and clustering with K-Means or HDBSCAN. …”
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Dimension Estimation Using Weighted Correlation Dimension Method by Yuanhong Liu, Zhiwei Yu, Ming Zeng, Shun Wang

“…Dimension reduction is an important tool for feature extraction and has been widely used in many fields including image processing, discrete-time systems, and fault diagnosis. …”
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Kernel Sliced Inverse Regression: Regularization and Consistency by Qiang Wu, Feng Liang, Sayan Mukherjee

“…Kernel sliced inverse regression (KSIR) is a natural framework for nonlinear dimension reduction using the mapping induced by kernels. …”
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An Adaptive Domain Partitioning Technique for Meshfree-Type Methods by Kamal Shanazari

“…A set of adaptive nodes is first generated using the dimension reduction and equidistributing along the coordinate directions with respect to arc-length monitor. …”
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Interpretability of Composite Indicators Based on Principal Components by Kris Boudt, Marco d’Errico, Hong Anh Luu, Rebecca Pietrelli

“…Principal component approaches are often used in the construction of composite indicators to summarize the information of input variables. The gain of dimension reduction comes at the cost of difficulties in interpretation, inaccurate targeting, and possible conflicts with the theoretical framework when the signs in the loading are not aligned with the expected direction of impact. …”
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Projections in Moduli Spaces of the Kleinian Groups by Hala Alaqad, Jianhua Gong, Gaven Martin

“…This gives a necessary condition in a simpler space to determine the discreteness of f,g. The dimension reduction here is realised by a projection of principal characters of the two-generator Kleinian groups. …”
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Research on CSI feedback of RIS-assisted massive MIMO system based on manifold learning by QIAN Mujun, YU Shunchi, LIU Chen, SONG Yunchao, LU Feng

“…Then, the framework combined the manifold learning to train two set of dictionaries to achieve dimension reduction and reconstruction of incremental CSI. …”
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Retinopathy Diabetic Recognition and Detection Using Novel Intelligent Algorithms by Liaquat Ali Rahoo

“…The proposed approach consists of four levels: pre-processing for noise removal and standardization of the input dataset, image segmentation using Spiking Neural Network (SNN) based on edge detection, dimension reduction and feature selection using percolation theory, and the final step of combining SNN and percolation theory for retinopathy area detection. …”
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