'Locality sensitive hashing' presentation slideshows

MapReduce and Frequent Itemsets Mining

MapReduce and Frequent Itemsets Mining. Kushaagra Goyal. MapReduce (Hadoop). Programming model designed for: Large Datasets (HDFS) Large files broken into chunks Chunks are replicated on different nodes Easy Parallelization Takes care of scheduling Fault Tolerance

1.12k views • 112 slides

CS598Visual Information retrieval

CS598Visual Information retrieval. Lecture VIII: LSH Recap and Case Study on Face Retrieval. Lecture VIII. Part I: Locality Sensitive Hashing Re-cap. Slides credit: Y. Gong. The problem. Large scale image search: Given a query image Want to search a large database to find similar images

976 views • 84 slides

Finding Similar Sets slides from Jeff Ullman

673 views • 54 slides

Privacy Preserving Outlier Detection using Locality Sensitive Hashing

Nisarg Raval , Madhuchand Rushi Pillutla, Piyush Bansal, K Srinathan and C V Jawahar IIIT Hyderabad, India. CSTAR. Privacy Preserving Outlier Detection using Locality Sensitive Hashing. Motivation. Motivation. Trusted Third Party (TTP). Motivation. Can we avoid TTP ?.

779 views • 50 slides

Finding Similar Items

Finding Similar Items. Set Similarity. Problem : Find similar sets. Motivation : Many things can be modeled/represented as sets Applications : Face Recognition Recommender Systems (Collaborative Filtering) Document Similarity/Clustering Similar (but infrequent) Items.

485 views • 38 slides

Finding Similar Items

Finding Similar Items. Set Similarity. Problem : Find similar sets. Motivation : Many things can be modeled/represented as sets Applications : Face Recognition Recommender Systems (Collaborative Filtering) Document Similarity/Clustering Similar (but infrequent) Items.

400 views • 38 slides

Scalable, Behavior-Based Malware Clustering

2009 NDSS. Scalable, Behavior-Based Malware Clustering. Ulrich Bayer ,Paolo Milani Comparetti ,Clemens Hlauschek ,Christopher Kruegel , and Engin Kirda Technical University Vienna University of California , Santa Brabara Institute Eurecom , Sophia Antipolis.

661 views • 36 slides

Ryan O'Donnell (CMU) Yi Wu (CMU, IBM) Yuan Zhou (CMU)

Optimal lower bounds for Locality Sensitive Hashing. (except when q is tiny). Ryan O'Donnell (CMU) Yi Wu (CMU, IBM) Yuan Zhou (CMU). Locality Sensitive Hashing [Indyk-Motwani '98]. h :. objects. sketches. H : family of hash functions h s.t.

526 views • 31 slides

Gate Sizing for Cell Library Based Designs

Gate Sizing for Cell Library Based Designs. Shiyan Hu*, Mahesh Ketkar**, Jiang Hu* *Dept of ECE, Texas A&M University **Intel Corporation. Outline. Introduction Motivation Problem Formulation Algorithms Continuous solution guided dynamic programming Node pruning and Stage pruning

445 views • 29 slides

Geometry of Similarity Search

295 views • 20 slides

Nearest Neighbor

317 views • 18 slides

Google News Personalization Scalable Online Collaborative Filtering

Google News Personalization Scalable Online Collaborative Filtering. Abhinandan Das - abhinandan@google.com Mayur Datar - mayur@google.com Ashutosh Garg - ashutosh@google.com Shyam Rajaram - rajaram1@ifp.uiuc.edu Presented by: Aniket Zamwar - zamwar@usc.edu. Already Studied in Class.

312 views • 16 slides

Approximate range selection queries in P2P systems

198 views • 9 slides