Kernel principal component analysis

Related lectures (31)

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

Explores the impact of hyperparameters on iso lines and the importance of centering data in kernel PCA.

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

Covers the analysis of principal components using matrices to approximate genetic data.

Explores unsupervised learning techniques for reducing dimensions in data, emphasizing PCA, LDA, and Kernel PCA.

Explores modeling neurobiological signals, focusing on spikes, firing rate, multiple state neurons, and parameter estimation.

Explores spectral clustering theory, eigenvalue decomposition, Laplacian matrix, and practical applications in identifying clusters.

Covers Principal Components Analysis, a technique for dimensionality reduction and gene type analysis.

Introduces Principal Component Analysis, focusing on maximizing variance in linear combinations to summarize data effectively.

Introduces the Young-Eckart-Mirsky theorem and PCA for unsupervised learning and data visualization.