Results 1 to 10 of about 919,275 (319)

Semi-sparse PCA [PDF]

Psychometrika, 2019
It is well known that the classical exploratory factor analysis (EFA) of data with more observations than variables has several types of indeterminacy. We study the factor indeterminacy and show some new aspects of this problem by considering EFA as a specific data matrix decomposition.
Eldén, Lars, Trendafilov, Nickolay
openaire   +3 more sources

Multi-lectin Affinity Chromatography and Quantitative Proteomic Analysis Reveal Differential Glycoform Levels between Prostate Cancer and Benign Prostatic Hyperplasia Sera. [PDF]

, 2018
Currently prostate-specific antigen is used for prostate cancer (PCa) screening, however it lacks the necessary specificity for differentiating PCa from other diseases of the prostate such as benign prostatic hyperplasia (BPH), presenting a clinical need
Adusumilli, Ravali, Brooks, James D, Kullolli, Majlinda, Mallick, Parag, Pitteri, Sharon J, Tanimoto, Cheylene, Totten, Sarah M   +6 more
core   +2 more sources

Spectral–Spatial Attention-Guided Multi-Resolution Network for Pansharpening

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Pansharpening is a technique that combines high-resolution panchromatic (PAN) images with low-resolution multispectral (MS) images to produce high-resolution MS (HRMS) images.
Shen Xu, Shengwei Zhong, Hui Li, Chen Gong   +3 more
doaj   +1 more source

A survey on deep learning for polyp segmentation: techniques, challenges and future trends

Visual Intelligence
Early detection and assessment of polyps play a crucial role in the prevention and treatment of colorectal cancer (CRC). Polyp segmentation provides an effective solution to assist clinicians in accurately locating and segmenting polyp regions.
Jiaxin Mei, Tao Zhou, Kaiwen Huang, Yizhe Zhang, Yi Zhou, Ye Wu, Huazhu Fu   +6 more
doaj   +1 more source

Mamba-Enhanced Background Suppression Diffusion Model for Hyperspectral Anomaly Detection

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Hyperspectral anomaly detection (HAD) faces a significant challenge in separating scarce, small and subtle anomalous targets from complex backgrounds.
Dan Sun, Shengwei Zhong, Chen Gong
doaj   +1 more source

Validation of nonlinear PCA

, 2012
Linear principal component analysis (PCA) can be extended to a nonlinear PCA by using artificial neural networks. But the benefit of curved components requires a careful control of the model complexity.
A Herman, A Ilin, AN Gorban, B Chalmond, B Christiansen, B Efron, B Schölkopf, BW Lu, D DeMers, JB Tenenbaum, LK Saul, M Scholz, MA Kramer, Matthias Scholz, MR Hestenes, ND Lawrence, P Demartines, R Hecht-Nielsen, S Girard, S Harmeling, S Mika, ST Roweis, T Hastie, T Kohonen, WW Hsieh, WW Hsieh, WW Hsieh   +26 more
core   +1 more source

PCA and t-SNE Implementation for KNN Hypertension Classification Visualization

Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
Hypertension is a condition that, if allowed to increase, can significantly injure internal organs due to high blood pressure. The objective of this study is to use the K-Nearest Neighbor (KNN) algorithm along with PCA and t-SNE to accurately identify ...
Andi Aulia Cahyana Resky, Jessica Crisfin Lapendy, Andi Akram Nur Risal, Dewi Fatmarani Surianto, Abdul Wahid   +4 more
doaj   +1 more source

A Model for Identifying Road Risk Class

Transport and Telecommunication, 2023
In many road safety, traffic management, and travel planning analyses, it is useful to classify road sections according to risk level. Such classification is labour-intensive and needs to be reviewed periodically.
Ryguła Artur, Brzozowski Krzysztof, Maczyński Andrzej   +2 more
doaj   +1 more source

Landslide Hazard Analysis Using a Multilayered Approach Based on Various Input Data Configurations

Geosfera Indonesia, 2021
Landslide is a natural disaster that occurs mostly in hill areas. Landslide hazard mapping is used to classify the prone areas to mitigate the risk of landslide hazards.
Ilyas Ahmad Huqqani, Tay Lea Tien, Junita Mohamad-Saleh   +2 more
doaj   +1 more source

PCA of PCA: Principal Component Analysis of Partial Covering Absorption in NGC 1365 [PDF]

, 2014
We analyse 400 ks of XMM-Newton data on the active galactic nucleus NGC 1365 using principal component analysis (PCA) to identify model independent spectral components.
Fabian, A. C., Parker, M. L., Risaliti, G., Walton, D. J.   +3 more
core   +2 more sources