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Lung Cancer Lesion Detection in Histopathology Images Using GraphâBased Sparse PCA Network [PDF]
Neoplasia, 2023 Early detection of lung cancer is critical for improvement of patient survival. To address the clinical need for efficacious treatments, genetically engineered mouse models (GEMM) have become integral in identifying and evaluating the molecular ...
Ravi Pal +2 more
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Dynamic Meta-Data Network Sparse PCA for Cancer Subtype Biomarker Screening [PDF]
Frontiers in Genetics, 2022 Previous research shows that each type of cancer can be divided into multiple subtypes, which is one of the key reasons that make cancer difficult to cure.
Xin-Yue Mei, Qi Dang, Shao Li
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A Guide for Sparse PCA: Model Comparison and Applications [PDF]
Psychometrika, 2021 PCA is a popular tool for exploring and summarizing multivariate data, especially those consisting of many variables. PCA, however, is often not simple to interpret, as the components are a linear combination of the variables. To address this issue, numerous methods have been proposed to sparsify the nonzero coefficients in the components, including ...
Katrijn van Deun +2 more
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Predicting DDI-induced pregnancy and neonatal ADRs using sparse PCA and stacking ensemble approach [PDF]
Journal of Integrative BioinformaticsPredicting Drug-Drug interaction (DDI)-induced adverse drug reactions (ADRs) using computational methods is challenging due to the availability of limited data samples, data sparsity, and high dimensionality.
Chaurasia Anushka, Kumar Deepak, Yogita
doaj +2 more sources
Visual Object Tracking Using Structured Sparse PCA-Based Appearance Representation and Online Learning [PDF]
Sensors, 2018 Visual object tracking is a fundamental research area in the field of computer vision and pattern recognition because it can be utilized by various intelligent systems.
Gang-Joon Yoon +2 more
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SuSiE PCA: A scalable Bayesian variable selection technique for principal component analysis
iScience, 2023 Summary: Latent factor models, like principal component analysis (PCA), provide a statistical framework to infer low-rank representation in various biological contexts. However, feature selection is challenging when this low-rank structure manifests from
Dong Yuan, Nicholas Mancuso
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Hyperspectral Image Denoising via Nonlocal Spectral Sparse Subspace Representation
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023 Hyperspectral image (HSI) denoising based on nonlocal subspace representation has attracted a lot of attention recently. However, most of the existing works mainly focus on refining the representation coefficient images (RCIs) using certain nonlocal ...
Hailin Wang +5 more
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ICT Express, 2023 To improve the acquisition speed and inbound capacity of the ground station in a burst direct-sequence (DS) spread-spectrum transmission system, an acquisition method based on a modified parallel code-phase acquisition (PCA) scheme is proposed. By taking
Chengyao Tang +4 more
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Online Tensor Robust Principal Component Analysis
IEEE Access, 2022 Online robust principal component analysis (RPCA) algorithms recursively decompose incoming data into low-rank and sparse components. However, they operate on data vectors and cannot directly be applied to higher-order data arrays (e.g. video frames). In
Mohammad M. Salut, David V. Anderson
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BMC Bioinformatics, 2017 Background Sparse principal component analysis (PCA) is a popular tool for dimensionality reduction, pattern recognition, and visualization of high dimensional data.
Ziyi Li, Sandra E. Safo, Qi Long
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