Independent component analysis

Applied sciences
Information engineering
Data science
Dimensionality reduction

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Related lectures (31)

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Principal Component Analysis: Dimensionality Reduction

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

Maximum Entropy Modeling: Applications & Inference

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

PCA: Interactive class

On PCA includes interactive exercises and emphasizes minimizing information loss.

Principal Component Analysis: Dimension Reduction

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

Principal Component Analysis: Properties and Applications

Explores Principal Component Analysis theory, properties, applications, and hypothesis testing in multivariate statistics.

Estimating the Term Structure: Principal Component Analysis

Covers Principal Component Analysis for yield curve shape estimation and dimension reduction in interest rate models.

Data Characterization: PCA & Spike Sorting

Explores PCA for data simplification and Spike Sorting for shape identification.

Atomic-scale Modeling: Interactive Exercises and Graded Assignments

Explores atomic-scale modeling through interactive exercises and graded assignments, covering crystallography, diffraction, defects, and regression models.

Principal Component Analysis: Geometric Interpretation and Dimension Reduction

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

PCA: Intuition

Covers the basics of PCA, exercises on dimensionality reduction, and criteria for choosing projections.