Applications of Kernel CCA

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Related lectures (32)

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Canonical Correlation Analysis: Linear and Kernel CCA

Introduces Canonical Correlation Analysis for finding joint features in separate datasets, extending to multimodal data and non-linear features.

PCA: Key Concepts

Covers the key concepts of Principal Component Analysis (PCA) and its practical applications in data dimensionality reduction and feature extraction.

PCA: Key Concepts

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

Image Processing I: Correlation Measures and Matched Filtering

Explores correlation measures, matched filtering, and feature extraction in image processing.

Machine Learning for Feature Extraction

Explores machine learning for feature extraction, 3D vision, and neural networks in mobile robotics.

Principal Component Analysis: Dimensionality Reduction

Covers Principal Component Analysis for dimensionality reduction, exploring its applications, limitations, and importance of choosing the right components.

Dimensionality Reduction: PCA & t-SNE

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

Perception: Data-Driven Approaches

Explores perception in deep learning for autonomous vehicles, covering image classification, optimization methods, and the role of representation in machine learning.

Canonical Correlation Analysis: Overview

Covers Canonical Correlation Analysis, a method to find relationships between two sets of variables.

Dependence Concepts and Copulas

Explores dependence concepts, copulas, correlation fallacies, and rank correlations in statistics.