Active Subspace Methods in Theory and Practice: Applications to Kriging Surfaces by Constantine, Paul G., Wang, Qiqi, Dow, Eric A.

“…We apply the method to an elliptic PDE model with coefficients parameterized by 100 Gaussian random variables and compare it with a local sensitivity analysis method for dimension reduction.…”
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Competency-based Feedback for the Improvement of Employment Outcomes for Computing Students by Palmer-Brown, Dominic, Cai, Fang Fang, Patel, Preeti

“…More importantly, it has been possible, through cluster analysis and dimension reduction, to optimise the competency framework to a condensed form which can be readily utilised throughout the work experience.…”
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Patient clustering for vital organ failure using ICD code with graph attention by Liu, Z, Hu, Y, Wu, X, Mertes, G, Yang, Y, Clifton, DA

“…We employ an autoencoder-based deep clustering architecture jointly trained with a K-means loss, and a non-linear dimension reduction is performed to obtain patient clusters on the MIMIC-III dataset. …”

Novel machine learning methods for cancer sequencing analysis by Feng, Y

“…</p> <p>In the work for high dimensional genomic data analysis, I developed an approach which jointly carries out dimension reduction and clustering. I showed that this method could recover the underlying latent structure of genomic sequencing data more accurately than existing methods and this method could be used for scRNA-seq data analysis as well as many other fields.…”

Smoothed particle hydrodynamics and its applications for multiphase flow and reactive transport in porous media by Trask, N., Tartakovsky, A. M., Pan, Kai, Jones, Bruce David, Williams, John R

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To which world regions does the valence–dominance model of social perception apply? by Lu, Jackson

“…Collectively, these results suggest that, while the valence–dominance model generalizes very well across regions when dimensions are forced to be orthogonal, regional differences are revealed when we use different extraction methods and correlate and rotate the dimension reduction solution. Protocol registration: The stage 1 protocol for this Registered Report was accepted in principle on 5 November 2018. …”
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Robust semi-supervised nonnegative matrix factorization for image clustering by Peng, Siyuan, Ser, Wee, Chen, Badong, Lin, Zhiping

“…Nonnegative matrix factorization (NMF) is a powerful dimension reduction method, and has received increasing attention in various practical applications. …”
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Deep residual pooling network for texture recognition by Mao, Shangbo, Rajan, Deepu, Chia, Liang Tien

“…These methods either produce high dimensional features or have multiple steps like dictionary learning, feature encoding and dimension reduction. In this paper, we propose a novel end-to-end learning framework that not only overcomes these limitations, but also demonstrates faster learning. …”
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Model predictive control for production scheduling problem in flexible manufacturing system by Xiao, Ziyao

“…The two numerical expressions are model one for simplicity and model two for dimension reduction. Then, based on the modelling method, MPC is capable of working out most kinds of production scheduling problems in the flexible manufacturing system. …”
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Machine learning approaches to predict drug responses in cancer from multi-omics data by Muhammad Zaki Bin Mohammad Bakri

“…Hence the approach we have taken to tackle this high dimensionality issue was by implementing a dimension reduction technique using Variational Autoencoders (VAE). …”
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ECG biometric authentication based on non-fiducial approach using kernel methods by Hejazi, Maryamsadat, Syed Mohamed, Syed Abdul Rahman Al-Haddad, Singh, Yashwant Prasad, Hashim, Shaiful Jahari, Abdul Aziz, Ahmad Fazli

“…At present, in order to generate a significant ECG feature set, non-fiducial methodologies based on an autocorrelation (AC) in conjunction with linear dimension reduction methods are used. This paper proposes a new non-fiducial framework for ECG biometric verification using kernel methods to reduce both high autocorrelation vectors' dimensionality and recognition system after denoising signals of 52 subjects with Discrete Wavelet Transform (DWT). …”
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Structural features with nonnegative matrix factorization for metamorphic malware detection by Yeong, Tyng Ling, Mohd Sani, Nor Fazlida, Abdullah, Mohd. Taufik, Abdul Hamid, Nor Asilah Wati

“…Overall, the proposed feature representations and the technique of dimension reduction can be useful for detecting metamorphic malware.…”
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Register Transfer Level Implementation Of Pooling - Based Feature Extraction For Finger Vein Identification by Mohamed Ali, Mohamed Hassan

“…With patch size = 4, four pyramid levels = [1x1, 2x2, 3x3, 4x4] and ~38 % dimension reduction on the extracted features vector (10 PCA coefficient), the accuracy of the identification was 88.69 % which was higher than PCA by 10.10%. …”
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Forecasting Multivariate Time Series Meteorological Data for Solar Thermal Cogeneration Systems by Tan, Lit Ken, Ong, Sie Meng, Nor Azwadi, Che Sidik, Asako, Yutaka, Lee, Kee Quen, Gan, Yee Siang, Goh, Chien Yong, Tey, Wah Yen, Ngien, S. K., Chuan, Zun Liang

“…In this research, a new methodology to forecast solar radiation via several meteorological data that incorporates dimension reduction technique is proposed. Based on the proposed methodology, two prediction models, Artificial Neural Network and statistical are established.…”
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EEG-based emotion recognition using deep learning by Samriddhi, Govil

“…Shannon entropy and wavelet energy were chosen as features for feature extraction. Dimension reduction was implemented through the use principal component analysis. …”
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Human action recognition using pose-based discriminant embedding by Saghafi, Behrouz, Rajan, Deepu

“…Moreover, the method outperforms other classical dimension reduction techniques and performs faster by choosing less number of postures.…”
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KLASIFIKASI DATA MICROARRAY MENGGUNAKAN DISCRETE WAVELET TRANSFORM DAN EXTREME LEARNING MACHINE by , KHADIJAH, , Dra. Sri Hartati, M.Sc., Ph.D.

“…The important things in the microarray data classification are the huge number of genes as dimension of microarray data (high dimensionality) and the limited sample size, so the method of dimension reduction and the classifier algorithm should be well determined. …”

Radial basis network estimator of oxygen content in the flue gas of debutanizer reboiler by Sembodo, Shafanda Nabil, Effendy, Nazrul, Dwiantoro, Kenny, Muddin, Nidlom

“…Stochastics gradient descent algorithm with momentum acceleration and dimension reduction using principal component analysis successfully improves the soft sensors' performance. © 2022 Institute of Advanced Engineering and Science. …”
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Fourier transform infrared spectrum pre-processing technique selection for detecting PYLCV-infected chilli plants by Agustika, Dyah K., Mercuriani, Ixora, Purnomo, Chandra W., Hartono, Sedyo, Triyana, Kuwat, Iliescu, Doina D., Leeson, Mark S.

“…The pre-processing was applied to the mid-infrared spectral range (4000 – 400 cm−1) and the biofingerprint region (1800 – 900 cm−1) then the discrete wavelet transform (DWT) was used for dimension reduction. The pre-processed data were then used as an input for classification using a multilayer perceptron neural network, a support vector machine, and linear discriminant analysis. …”
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Single-cell analysis of alternative splicing in normal and malignant stem/progenitor cells by Wen, S

“…MARVEL is an R package that provides comprehensive functionalities for the detection and quantification of alternative splicing events to enable dimension reduction analysis, differential splicing analysis, and functional annotation of differentially spliced genes. …”