Concept
Kernel principal component analysis
Related lectures (68)
Principal Component Analysis: Understanding Data Structure
Explores Principal Component Analysis, dimensionality reduction, data quality assessment, and error rate control.
Machine Learning Fundamentals
Covers key concepts and examples of machine learning algorithms and techniques.
Understanding Autoencoders
Explores autoencoders, from linear mappings in PCA to nonlinear mappings, deep autoencoders, and their applications.
Principal Component Analysis: Dimensionality Reduction
Explores Principal Component Analysis for dimensionality reduction and unsupervised feature selection.
Neural Networks Recap: Activation Functions
Covers the basics of neural networks, activation functions, training, image processing, CNNs, regularization, and dimensionality reduction methods.
Principal Component Analysis: Eigenfaces
Covers the application of Principal Component Analysis in facial recognition using a famous faces dataset.
Dimensionality Reduction: PCA and Kernel PCA
Covers PCA, Kernel PCA, and autoencoders for dimensionality reduction in data analysis.
Neurobiological Signal Processing
Explores neurobiological signal processing, covering spike modeling, signal classification, and data characterization using principal component analysis.
Principal Component Analysis: Dimensionality Reduction
Explores Principal Component Analysis for dimensionality reduction in machine learning, showcasing its feature extraction and data preprocessing capabilities.
Financial Time Series: ARCH and GARCH Models
Covers regression analysis, multivariate linear regression, principal component analysis, and factor models.