Project Progress

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Project Progress

  1. 1. Project Progress
  2. 2. What we’ve been doing(1) • Hacking Hadoop API. • Writing different kinds of programs to understand it. (Not CV programs) • Adaboost • SIFT, SURF • Reading, Reading
  3. 3. Segmentation ROI ROI
  4. 4. segmentation with overlap get SIFT/SURF descriptor for partial segments reduce no. of descriptors by grouping them. region of interest (positive&negative) count the frequency of occurrence of visual words AdaBoost
  5. 5. Methodology • For simplicity, assume the the same image is stored on all slave nodes. • Use ROI to run the algorithm. • Hopefully this will make it easier for the “Reduce”
  6. 6. Map-Reduce??? • It’s just a framework • You can also implement it by reading the paper[1]. :) • Hadoop is one implementation. (Apache + Yahoo) • Google’s implementation is not made public.
  7. 7. Map-Reduce for Machine Learning on Multi-core
  8. 8. Introduction • Algorithm fitting Statistical Query Model may be written in a certain “summation form” • Divide into data set into as many pieces as the number of cores.
  9. 9. • Algorithm fitting Statistical Query Model may be written in a certain “summation form” • Divide into data set into as many pieces as the number of cores.
  10. 10. Algorithms(1) • Locally Weight Linear Regression • Naive Bayes • Gaussian Discriminative Analysis • k-means • Logistic Regression • Neural Network
  11. 11. Algorithms(2) • Principal Components Analysis • Independent Components Analysis • Expansion Maximization • Support Vector Machine
  12. 12. Example (LWLR) divide the computation among different mappers to compute: 2 reducers sum up the partial values for A and b and finally computes the solution
  13. 13. Experiment Result • Used UCI Machine Learning repository • Used only 2 cores. • 1.9x times faster • 54 times speed up on 64 cores. • Speed up is achieved by “throwing cores” only

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