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What is Principal Component Analysis (PCA) and how are eigenvalues and eigenvectors used in it?
What is Principal Component Analysis (PCA) and how are eigenvalues and eigenvectors used in it?
Principal Component Analysis (PCA) is a technique used to reduce the dimensionality of data while preserving its variance. Eigenvalues and eigenvectors are used in PCA to achieve this dimensionality reduction by identifying the principal components of the data.
How are eigenvectors applied in Graph Theory, particularly in network analysis?
How are eigenvectors applied in Graph Theory, particularly in network analysis?
Eigenvectors are utilized in Graph Theory to analyze the structure of graphs. In network analysis, the leading eigenvector of the adjacency matrix is used to identify influential nodes in a network.
In what ways are eigenvalues and eigenvectors utilized in Image and Signal Processing?
In what ways are eigenvalues and eigenvectors utilized in Image and Signal Processing?
Eigenvalues and eigenvectors are utilized in image compression, denoising, and analysis. In signal processing, they are used for tasks like filtering and feature extraction.
How are eigenvalues and eigenvectors employed in Machine Learning algorithms?
How are eigenvalues and eigenvectors employed in Machine Learning algorithms?
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What role do eigenvalues and eigenvectors play in Linear Algebra Operations, particularly in computer graphics?
What role do eigenvalues and eigenvectors play in Linear Algebra Operations, particularly in computer graphics?
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