Armadillo, a C++ linear algebra library, offers built-in machine learning capabilities to simplify complex data analysis tasks. K-means clustering and principal component analysis (PCA) are two prominent techniques supported by Armadillo.
K-means clustering is an unsupervised learning algorithm that partitions data into ‘k’ distinct clusters based on similarity measures. It iteratively assigns each data point to the nearest centroid and updates centroids until convergence. Armadillo’s k-means implementation efficiently handles large datasets and supports customizable distance metrics for diverse applications.
Principal component analysis (PCA) is a dimensionality reduction technique that transforms data into a new coordinate system with orthogonal axes. By retaining only the most significant components, PCA reduces noise and computational complexity while preserving essential information. Armadillo’s PCA functionality computes eigenvectors and eigenvalues of the covariance matrix, enabling efficient extraction of principal components.
Both k-means clustering and PCA in Armadillo facilitate streamlined integration with other linear algebra operations, providing a robust foundation for advanced machine learning applications.
