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Lecture
Dimensionality Reduction
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Related lectures (50)
Principal Components: Properties & Applications
Explores principal components, covariance, correlation, choice, and applications in data analysis.
Principal Component Analysis: Dimension Reduction
Covers Principal Component Analysis for dimension reduction in biological data, focusing on visualization and pattern identification.
Principal Component Analysis: Dimensionality Reduction
Covers Principal Component Analysis for dimensionality reduction, exploring its applications, limitations, and importance of choosing the right components.
Canonical Correlation Analysis: Overview
Covers Canonical Correlation Analysis, a method to find relationships between two sets of variables.
Unsupervised learning: Young-Eckart-Mirsky theorem and intro to PCA
Introduces the Young-Eckart-Mirsky theorem and PCA for unsupervised learning and data visualization.
Principal Component Analysis: Introduction
Introduces Principal Component Analysis, focusing on maximizing variance in linear combinations to summarize data effectively.
Multivariate Methods I
Explores multivariate methods like PCA, SVD, PLS, and ICA for dimensionality reduction in functional brain imaging.
Singular Value Decomposition
Explores Singular Value Decomposition and its role in unsupervised learning and dimensionality reduction, emphasizing its properties and applications.
Clustering Methods and Dimensionality Reduction
Covers clustering methods and dimensionality reduction techniques.
Linear Dimensionality Reduction
Explores linear dimensionality reduction through PCA, variance maximization, and real-world applications like medical data analysis.
Spectral Clustering: Theory and Applications
Explores spectral clustering theory, eigenvalue decomposition, Laplacian matrix, and practical applications in identifying clusters.
SVD: Singular Value Decomposition
Covers the concept of Singular Value Decomposition (SVD) for compressing information in matrices and images.
Singular Value Decomposition: Theory and Applications
Explores Singular Value Decomposition theory, properties, uniqueness, matrix approximation, and dimensionality reduction applications.
PCA: Directions of Largest Variance
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Covers PCA, finding directions of largest variance, data dimensionality reduction, and limitations of PCA.
Unsupervised Learning: PCA & K-means
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Covers unsupervised learning with PCA and K-means for dimensionality reduction and data clustering.
Principal Component Analysis: Applications and Limitations
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Explores the applications and limitations of Principal Component Analysis, including denoising, compression, and regression.
Singular Value Decomposition: Applications and Interpretation
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Explains the construction of U, verification of results, and interpretation of SVD in matrix decomposition.
Principal Component Analysis: Olympic Medals & Image Compression
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Explores PCA for predicting medals distribution and compressing face images.
Unsupervised Learning: Principal Component Analysis
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Covers unsupervised learning with a focus on Principal Component Analysis and the Singular Value Decomposition.
Unsupervised Learning: Movie Recommendation
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Covers unsupervised learning for movie recommendation using singular value decomposition.
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