PCA and Kernel PCA

Related lectures (31)

Page 3 of 4

Covers the theory and practice of clustering algorithms, including PCA, K-means, Fisher LDA, spectral clustering, and dimensionality reduction.

Explores principal components, covariance, correlation, choice, and applications in data analysis.

Introduces unsupervised learning through clustering with K-means and dimensionality reduction using PCA, along with practical examples.

Explores dimensionality reduction, clustering algorithms, and the state of machine learning.

Covers the basics of PCA, exercises on dimensionality reduction, and criteria for choosing projections.

Covers dimensionality reduction techniques like PCA and LDA, clustering methods, density estimation, and data representation.

Covers the key concepts of PCA, including reducing data dimensionality and extracting features, with practical exercises.

Introduces artificial neural networks, CNNs, and dimensionality reduction using PCA and autoencoders.

Explores Principal Component Analysis for dimension reduction and data representation in a new basis.

Covers Principal Component Analysis for dimension reduction in biological data, focusing on visualization and pattern identification.