Principal component analysis (PCA) is a statistical technique for reducing the dimensionality of a dataset by linearly transforming it into a new coordinate system. It is used to maximize the amount of information preserved while making data more interpretable and enabling visualization of multidimensional data. PCA is commonly used for dimensionality reduction, projecting each data point onto only the first few principal components to obtain lower-dimensional data while preserving as much of the data's variation as possible.
Princeton University
Spring 2019
This introductory course focuses on machine learning, probabilistic reasoning, and decision-making in uncertain environments. A blend of theory and practice, the course aims to answer how systems can learn from experience and manage real-world uncertainties.
No concepts data
+ 21 more concepts