Principal component analysis

Related lectures (30)

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Explores textual data classification, focusing on methods like Naive Bayes and dimensionality reduction techniques like Principal Component Analysis.

Covers distributions and techniques in Quantitative Risk Management for financial modeling.

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

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

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

Introduces Taylor polynomials for approximating functions around a point, showcasing their importance in accurately representing functions.

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

Explores Principal Component Analysis for dimensionality reduction and unsupervised feature selection.

Explores maximum entropy modeling applications in neuroscience and protein sequence data.

Covers Principal Component Analysis for dimension reduction in biological data, focusing on visualization and pattern identification.