Locality-sensitive hashing

Applied sciences
Information engineering
Data science
Dimensionality reduction

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Related lectures (8)

Nearest Neighbor Search: Johnson-Lindenstrauss Lemma

Covers the Nearest Neighbor search algorithm and the Johnson-Lindenstrauss lemma for dimensionality reduction, exploring preprocessing techniques and locality-sensitive hashing.

Near Neighbors Retrieval: Efficient Techniques

Covers techniques for efficiently retrieving similar items using similarity search queries.

Pairwise Independence: Hashing and Load Balancing

Explores pairwise independence in hashing to avoid collisions and achieve load balancing.

Curse of Dimensionality in Deep Learning

Delves into the challenges of deep learning, exploring dimensionality, performance, and overfitting phenomena in neural networks.

Locality Sensitive Hashing

Explores Locality Sensitive Hashing for nearest neighbor search and submodularity in hash functions.

Feature Learning: Stability and Curse of Dimensionality

Explores how modern architectures beat the curse of dimensionality and the importance of stability in deep learning models.

Graph metrics: Statistical analysis

Explores graph metrics and statistical analysis in network clustering, including ERGMs application in sociology and asymptotics.

Privacy-preserving data publishing: K-anonymity and l-Diversity

Explores K-anonymity, l-Diversity, and data de-identification challenges, using real-life examples and discussing Airbnb's privacy efforts.

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