Concept
Eigenface
Related lectures (7)
Principal Component Analysis: Eigenfaces
Covers the application of Principal Component Analysis in facial recognition using a famous faces dataset.
Clustering: K-means & LDA
Covers clustering using K-means and LDA, PCA, K-means properties, Fisher LDA, and spectral clustering.
Shape From Stereo-2
Explores stereo vision concepts such as occlusions, window size impact, multi-view stereo, dynamic shape reconstruction, and graph-based segmentation.
Linear Dimensionality Reduction
Explores linear dimensionality reduction through PCA, variance maximization, and real-world applications like medical data analysis.
Linear Dimensionality Reduction: PCA and LDA
Explores PCA and LDA for linear dimensionality reduction in data, emphasizing clustering and class separation techniques.
Clustering: Unsupervised Learning
Explores dimensionality reduction, clustering algorithms, and the state of machine learning.
Clustering: Theory and Practice
Covers the theory and practice of clustering algorithms, including PCA, K-means, Fisher LDA, spectral clustering, and dimensionality reduction.