The document discusses using MapReduce for machine learning algorithms on multi-core systems. It describes segmenting images into regions of interest for feature extraction using SIFT and SURF descriptors. Machine learning algorithms like AdaBoost, locally weighted linear regression, naive Bayes, and support vector machines are proposed to fit the MapReduce model by dividing data and computation across multiple cores. Experimental results show nearly 2x speedup on 2 cores and 54x speedup on 64 cores by parallelizing algorithm computations.

1. Project Progress

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. Segmentation
ROI ROI

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. 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. 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. Map-Reduce for Machine
Learning on Multi-core

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. • 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. Algorithms(1)
• Locally Weight Linear Regression
• Naive Bayes
• Gaussian Discriminative Analysis
• k-means
• Logistic Regression
• Neural Network

11. Algorithms(2)
• Principal Components Analysis
• Independent Components Analysis
• Expansion Maximization
• Support Vector Machine

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. 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