Applications of Kernel CCA

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Related lectures (32)

Page 3 of 4

Transformations of Input or Output

Covers handling missing data, feature engineering, and output transformations in machine learning.

Dimensionality Reduction: PCA and Autoencoders

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

Support Vector Clustering: SVC

Introduces Support Vector Clustering (SVC) using a Gaussian kernel for high-dimensional feature space mapping and explains its constraints and Lagrangian.

Self-supervised learning

Explores self-supervised learning, embedding techniques, HSIC, and geometric information in downstream tasks.

Efficient Data Clustering

Covers efficient data exploitation through clustering methods and the optimization of market returns using asset clustering.

Dimensionality Reduction: PCA & LDA

Covers PCA and LDA for dimensionality reduction, explaining variance maximization, eigenvector problems, and the benefits of Kernel PCA for nonlinear data.

Clustering: Dimensionality Reduction

Explores clustering and dimensionality reduction techniques in finance to clean and simplify data.

Principal Component Analysis: Eigenfaces

Covers the application of Principal Component Analysis in facial recognition using a famous faces dataset.

Kernel K-means: Advanced Machine Learning

Introduces Kernel K-means, extending K-means to create non-linear separations of data points.

Dimensionality Reduction: PCA and LDA

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