Description
Principle Components Analysis (PCA) is an unsupervised method primary used for dimensionality reduction within machine learning. PCA is calculated via a singular value decomposition (SVD) of the design matrix, or alternatively, by calculating the covariance matrix of the data and performing eigenvalue decomposition on the covariance matrix. The results of PCA provide a low-dimensional picture of the structure of the data and the leading (uncorrelated) latent factors determining variation in the data.
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Papers Using This Method
Lightweight Model for Poultry Disease Detection from Fecal Images Using Multi-Color Space Feature Optimization and Machine Learning2025-07-14Hybrid-View Attention for csPCa Classification in TRUS2025-07-04Causal discovery in deterministic discrete LTI-DAE systems2025-06-25RAG-VisualRec: An Open Resource for Vision- and Text-Enhanced Retrieval-Augmented Generation in Recommendation2025-06-25PCA-Guided Quantile Sampling: Preserving Data Structure in Large-Scale Subsampling2025-06-23Construction of an Organ Shape Atlas Using a Hierarchical Mesh Variational Autoencoder2025-06-18ROSAQ: Rotation-based Saliency-Aware Weight Quantization for Efficiently Compressing Large Language Models2025-06-16Beyond Sin-Squared Error: Linear-Time Entrywise Uncertainty Quantification for Streaming PCA2025-06-14Advanced fraud detection using machine learning models: enhancing financial transaction security2025-06-12Analyzing Emotions in Bangla Social Media Comments Using Machine Learning and LIME2025-06-11Optimizing Genetic Algorithms with Multilayer Perceptron Networks for Enhancing TinyFace Recognition2025-06-11Diffusion index forecasts under weaker loadings: PCA, ridge regression, and random projections2025-06-11Olica: Efficient Structured Pruning of Large Language Models without Retraining2025-06-10sparseGeoHOPCA: A Geometric Solution to Sparse Higher-Order PCA Without Covariance Estimation2025-06-10CommSense: A Rapid and Accurate ISAC Paradigm2025-06-09On the Wasserstein Geodesic Principal Component Analysis of probability measures2025-06-04Riemannian Principal Component Analysis2025-05-30PCA for Enhanced Cross-Dataset Generalizability in Breast Ultrasound Tumor Segmentation2025-05-29Machine Learning-Based Anomaly Detection of Correlated Sensor Data: An Integrated Principal Component Analysis-Autoencoder Approach2025-05-29Kernel-Smoothed Scores for Denoising Diffusion: A Bias-Variance Study2025-05-28