'Random projection' diaporamas de présentation

High Performance Correlation Techniques For Time Series

High Performance Correlation Techniques For Time Series. Xiaojian Zhao Department of Computer Science Courant Institute of Mathematical Sciences New York university 25 Oct. 2004. Roadmap. Section 1: Introduction Motivation Problem Statement Section 2 : Background GEMINI framework

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High Performance Algorithms for Multiple Streaming Time Series

High Performance Algorithms for Multiple Streaming Time Series. Xiaojian Zhao Advisor: Dennis Shasha Department of Computer Science Courant Institute of Mathematical Sciences New York University Jan. 10 2006. Roadmap:. Motivation Incremental Uncooperative Time Series Correlation

756 views • 64 slides

Compressive Signal Processing

Compressive Signal Processing. Richard Baraniuk Rice University. Better, Stronger, Faster. Analog sensing Digital sensing. Sense by Sampling. sample. Sense by Sampling. too much data!. sample. Case in Point: DARPA ARGUS-IS. 1.8 Gpixel image sensor

788 views • 63 slides

Motif Finding

Motif Finding. Yueyi Irene Liu CS374 Lecture Oct. 17, 2002. Outline. Background biology Motif-finding methods Word enumeration Gibbs sampling Random projection Phylogenetic footprinting Reducer. Regulation of Gene Expression. Chromatin structure Transcription initiation

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Efficient Triangle Counting in Large Graphs via Degree-based partitioning

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CS276A Text Retrieval and Mining

CS276A Text Retrieval and Mining. Lecture 15. Thanks to Thomas Hoffman, Brown University for sharing many of these slides. Recap: Clustering 2. Hierarchical clustering Agglomerative clustering techniques Evaluation Term vs. document space clustering Multi-lingual docs Feature selection

561 views • 43 slides

Randomized Algorithms Chapter 12

Randomized Algorithms Chapter 12. Jason Eric Johnson Presentation #3 CS6030 - Bioinformatics. In General. Make random decisions in operation Non-deterministic sequence of operations No input reliably gives worst-case results. Sorting. Classic Quicksort Can be fast - O(n log n)

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Motif Refinement using Hybrid Expectation Maximization Algorithm

Motif Refinement using Hybrid Expectation Maximization Algorithm. Chandan Reddy Yao-Chung Weng Hsiao-Dong Chiang School of Electrical and Computer Engr. Cornell University, Ithaca, NY - 14853. Motif Finding Problem.

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FACE RECOGNITION, EXPERIMENTS WITH RANDOM PROJECTION

FACE RECOGNITION, EXPERIMENTS WITH RANDOM PROJECTION. Navin Goel Graduate Student. Advisor: Dr George Bebis Associate Professor. Department Of Computer Science and Engineering University of Nevada, Reno. Overview. Introduction and Thesis Scope Principal Component Analysis

516 views • 27 slides

FACE RECOGNITION, EXPERIMENTS WITH RANDOM PROJECTION

FACE RECOGNITION, EXPERIMENTS WITH RANDOM PROJECTION. Navin Goel Graduate Student. Advisor: Dr George Bebis Associate Professor. Department Of Computer Science and Engineering University of Nevada, Reno. Overview. Introduction and Thesis Scope Principal Component Analysis

288 views • 27 slides

Dimensionality Reduction

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Shengyu Zhang The Chinese University of Hong Kong

On the power of a unique quantum witness. Shengyu Zhang The Chinese University of Hong Kong (Joint work with Rahul Jain, Iordanis Kerenidis, Greg Kuperberg, Miklos Santha, Or Sattash). Role of # of witnesses in NP. NP: Problems that can be verified in poly. time.

332 views • 21 slides

Random Projection Approach to Motif Finding

Random Projection Approach to Motif Finding. Adapted from http://genome.ucsd.edu/classes/be202/ppt/FindingSignals-RandomProjections.ppt. daf-19 Binding Sites in C. elegans (Peter Swoboda). GTT GT CATGGT G AC GTT T CCATGG A AAC G C T A CCATGG C AAC GTT A CCAT A GTAAC

347 views • 20 slides

Random Projection for High Dimensional Data Clustering: A Cluster Ensemble Approach

190 views • 17 slides