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Invidious > Channel > Victor Lavrenko

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YT 3 minutes 49 seconds Victor Lavrenko PCA 1: Real dimensionality vs observed dimensionality YT 3 minutes 55 seconds Victor Lavrenko PCA 2: Data manifolds in high-dimensional spaces YT 2 minutes 48 seconds Victor Lavrenko PCA 3: The curse of dimensionality

YT 3 minutes 59 seconds Victor Lavrenko PCA 4: Tackling the curse of dimensionality YT 2 minutes 22 seconds Victor Lavrenko PCA 5: Feature selection and feature extraction YT 2 minutes 39 seconds Victor Lavrenko PCA 6: Principal component analysis

YT 3 minutes Victor Lavrenko PCA 7: Why we maximize variance in PCA YT 8 minutes 26 seconds Victor Lavrenko PCA 8: Principal components = eigenvectors YT 5 minutes 21 seconds Victor Lavrenko PCA 9: Finding eigenvalues and eigenvectors

YT 2 minutes 14 seconds Victor Lavrenko PCA 10: Low-dimensional projections of data YT 11 minutes 55 seconds Victor Lavrenko PCA 11: Eigenvector = direction of maximum variance YT 6 minutes 3 seconds Victor Lavrenko PCA 12: Eigenvalue = variance along eigenvector

YT 3 minutes 57 seconds Victor Lavrenko PCA 13: How many principal components to use? YT 3 minutes 49 seconds Victor Lavrenko PCA 14: Principal component analysis for the impatient YT 5 minutes 1 second Victor Lavrenko PCA 15: Eigen-faces

YT 3 minutes 57 seconds Victor Lavrenko PCA 16: Eigenface representation YT 2 minutes 18 seconds Victor Lavrenko PCA 17: Properties of eigenfaces YT 1 minute 52 seconds Victor Lavrenko PCA 18: When principal components fail

YT 1 minute 48 seconds Victor Lavrenko PCA 19: Classification with PCA features YT 2 minutes 59 seconds Victor Lavrenko PCA 20: Linear discriminant analysis YT 1 minute 59 seconds Victor Lavrenko PCA 21: Pros and cons of dimensionality reduction