Dimensionality Reduction
Dimensionality reduction is a technique in machine learning and data science for reducing the number of dimensions of a feature space, aiming to compress redundancy while preserving the main information in the data. Common methods include Principal Component Analysis (PCA).
Timeline (last 90 days)
Eigendecomposition is closely related to principal component analysis (PCA) and is the basis of dimensionality reduction.
Principal component analysis (PCA) achieves dimensionality reduction by finding the few linearly independent directions with the greatest variance in the data and approximating the original data with low-dimensional coordinates.
Unsupervised learning deals with data that has no labels, and the model discovers structure in the data on its own, such as clustering and dimensionality reduction.
All Facts (3)
Eigendecomposition is closely related to principal component analysis (PCA) and is the basis of dimensionality reduction.
50%UnverifiedPrincipal component analysis (PCA) achieves dimensionality reduction by finding the few linearly independent directions with the greatest variance in the data and approximating the original data with low-dimensional coordinates.
50%UnverifiedUnsupervised learning deals with data that has no labels, and the model discovers structure in the data on its own, such as clustering and dimensionality reduction.
50%