Rank: 1933 based on 73 downloads (last 30 days) and 4 files submitted
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michael kim

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Company/University
Virginia Tech

Personal Profile:

I use math and data to solve problems.
My academic background is mostly in statistics, economics, EE/CS and OR. I am currently a PhD student.

Professional Interests:
signal processing, statistics

 

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Files Posted by michael View all
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(last 30 days)
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11 Nov 2013 Robust Multidimensional scaling (MDS) using ECDFs Take m points in R^n and transform those points to R^p such that the ECDF of distances are preserved Author: michael kim mds, multidimensional scal..., ecdf, robust, l1 norm, random projections 10 0
05 Nov 2013 Screenshot Linear time Outlier Scoring via Random Walks I use a markov chain rejection sampling method to score outliers in linear time. Author: michael kim outlier detection, anomalies, random walks, mcmc, rejection sampling, markov chains 14 0
28 Oct 2013 Screenshot Robust Association Testing via Resampling This is an attempt to test if two ECDF are independent Author: michael kim correlation, association, robustness, independence 10 0
27 Dec 2012 Screenshot Anomaly Detection Given m points in R^n (as a matrix), find the outliers via dimensionality reduction and resampling. Author: michael kim kolmogorov smirnov, statistics, machine learning, time series, outlier, anomaly detection 39 3
  • 3.0
3.0 | 1 rating
Comments and Ratings by michael View all
Updated File Comments Rating
07 Jan 2013 Anomaly Detection Given m points in R^n (as a matrix), find the outliers via dimensionality reduction and resampling. Author: michael kim

The code is documented here:
https://docs.google.com/open?id=0B9IkyvYlZZe7T3JhX1I0N3Nydlk

27 Dec 2012 Anomaly Detection Given m points in R^n (as a matrix), find the outliers via dimensionality reduction and resampling. Author: michael kim

Please use this link until the code is updated to reflect some changes: https://docs.google.com/open?id=0B9IkyvYlZZe7R3lDS21scmRieTA

Comments and Ratings on michael's Files View all
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29 May 2013 Anomaly Detection Given m points in R^n (as a matrix), find the outliers via dimensionality reduction and resampling. Author: michael kim Han

nice but too slow. a MEXed version might be faster?

07 Jan 2013 Anomaly Detection Given m points in R^n (as a matrix), find the outliers via dimensionality reduction and resampling. Author: michael kim kim, michael

The code is documented here:
https://docs.google.com/open?id=0B9IkyvYlZZe7T3JhX1I0N3Nydlk

27 Dec 2012 Anomaly Detection Given m points in R^n (as a matrix), find the outliers via dimensionality reduction and resampling. Author: michael kim kim, michael

Please use this link until the code is updated to reflect some changes: https://docs.google.com/open?id=0B9IkyvYlZZe7R3lDS21scmRieTA

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