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Chicago Machine Learning Meetup

  • Nov 13, 2012 · 5:30 PM
  • This location is shown only to members


Talk title: An introduction to Generalized K-means with image processing applications

In this era of digital Big Data, large scale collections of digital images are proliferating all over the place on and offline. For example

• users on Facebook upload more than 200 million photos every day
• in the medical imaging domain, over 68 million CT scans were performed in the US last year
• IT giants are building enormous visual maps of the world from massive collections of street view images

This explosive growth in image data poses serious challenges in terms of both storage - that is, how do we more efficiently compress rapidly growing collections of images? - and search - that is, how can we more effectively sort through image databases? - and in each case the best solutions developed so far rely heavily on machine learning techniques. Generalized K-means (G-K-means), more commonly called Dictionary Learning, is one of the key machine learning tools researchers such as my self are using to attack the storage problem. While at a high level this technique is really just exactly what you'd expect it to be - a generalized version of the standard K-means where you assign a data point to multiple clusters instead of just one - algorithm-wise it falls into the bucket of modern sparse statistical methods (e.g. compressive sensing, the lasso) which have been mentioned at some previous meetups.

This talk will be a user-friendly introduction to G-K-means with a practical algorithmic and application focus. I'll first review the standard K-means algorithm and its popular application to single image compression. I'll then show how, viewing K-means as a sparse statistical method, you can easily derive the analogous G-K-means model along with a natural greedy algorithm for solving it. Finally I'll show some cool applications of G-K-means to the processing of large databases of images, and discuss its application to the storage problem - that is, to large scale image collection compression.

Jeremy has a masters in math and is a current PhD student in Electrical Engineering at Northwestern. He conducts research in data mining/machine learning, and particularly in sparse statistical models and their applications to large scale image processing and business intelligence. He keeps a blog about research and teaching in the Machine Learning space located at

http://neonwatty.wordpress.com/

In his spare time he drinks coffee like there's no tomorrow, teaches self-designed courses in math and engineering to K-12 students through Northwestern University's Center for Talent Development, writes for the public radio program “A Moment of Science”, and takes an occasional improv class at Second City.

 

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  • Christopher S.

    This was my first time at this meetup and at any meetup I had a great experience with the talk and discussing generalized k-means among the attendants afterwards.

    November 14, 2012

    • H D.

      This is a wonderful meetup! Possible to have it on weekends? I'm not working in the city it's hard for me to make it, others may also have this problem. Thanks!

      November 14, 2012

  • A former member
    A former member

    Very interesting!

    November 13, 2012

  • Ameena L.

    Great as usual. Today's topic was very interesting and presenter did excellent job of explaining mathematical equations.

    November 13, 2012

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