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Principal component analysis (PCA) is a linear dimensionality reduction technique with applications in exploratory data analysis, visualization and data preprocessing. The data are …

Nov 13, 2025 · PCA (Principal Component Analysis) is a dimensionality reduction technique and helps us to reduce the number of features in a dataset while keeping the most important …

Principal component analysis, or PCA, reduces the number of dimensions in large datasets to principal components that retain most of the original information. It does this by transforming …

Jun 23, 2025 · Principal component analysis (PCA) is a technique that reduces the number of variables in a data set while preserving key patterns and trends. It simplifies complex data, …

Principal Component Analysis (PCA) takes a large data set with many variables per observation and reduces them to a smaller set of summary indices. These indices retain most of the …

Principal component analysis (PCA) is a technique used to emphasize variation and bring out strong patterns in a dataset. It's often used to make data easy to explore and visualize.

Principal component analysis (PCA). Linear dimensionality reduction using Singular Value Decomposition of the data to project it to a lower dimensional space. The input data is …

Jun 8, 2024 · In this article, I show the intuition of the inner workings of the PCA algorithm, covering key concepts such as Dimensionality Reduction, eigenvectors, and eigenvalues, then …

Principal Component Analysis (PCA) is a mathematical algorithm in which the objective is to reduce the dimensionality while explaining the most of the variation in the data set.