Mean shift

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Information engineering
Machine learning
Unsupervised learning

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Related lectures (9)

Clustering & Density Estimation

Covers dimensionality reduction, PCA, clustering techniques, and density estimation methods.

Clustering & Density Estimation

Covers clustering, PCA, LDA, K-means, GMM, KDE, and Mean Shift algorithms for density estimation and clustering.

Kernel K-means: Advanced Machine Learning

Introduces Kernel K-means, extending K-means to create non-linear separations of data points.

Clustering & Density Estimation

Covers dimensionality reduction, clustering, and density estimation techniques, including PCA, K-means, GMM, and Mean Shift.

Dimensionality Reduction: PCA and LDA

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

Quantization of Probability Distributions

Covers quantization of probability distributions, statistical k-means clustering, mean estimation, robust clustering methods, and open research questions.

Percolation: Bond Percolation

Covers bond percolation on a square lattice, discussing percolation phases, critical threshold, mean cluster size, and critical point scenarios.

K-means Clustering: Lloyd's Algorithm and RGB Space

Explains K-means clustering with Lloyd's algorithm and RGB space for color segmentation.

Clustering Methods

Covers K-means, hierarchical, and DBSCAN clustering methods with practical examples.

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