Nonlinear Dimension Reduction Methods

Related lectures (30)

Page 2 of 3

Covers clustering, PCA, LDA, K-means, GMM, KDE, and Mean Shift algorithms for density estimation and clustering.

Covers dimensionality reduction, clustering, and density estimation techniques, including PCA, K-means, GMM, and Mean Shift.

Explores PCA and t-SNE for reducing dimensions and visualizing high-dimensional data effectively.

Covers PCA, finding directions of largest variance, data dimensionality reduction, and limitations of PCA.

Introduces linear models for supervised learning, covering overfitting, regularization, and kernels, with applications in machine learning tasks.

Explores multivariate methods like PCA, SVD, PLS, and ICA for dimensionality reduction in functional brain imaging.

Explores linear dimensionality reduction through PCA, variance maximization, and real-world applications like medical data analysis.

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.