Private:progress-dameh

Fall 2011 (RA)

Oct 12

• Process Very-High dimensional and Very-Large Scale real DataSets
  • I chose document clustering, where each document i is commonly represented by a term-frequency vector xi = [x1i, x2i, ..., xDi], where D is the total number of keywords in the given document corpus, and xji is the number of occurrences of keyword j in document i. Keywords correspond to the words that remain after the pre-processing of stop-words removal and words stemming. Below N is the total number of documents.
• DataSets: source: http://archive.ics.uci.edu/ml/datasets/Bag+of+Words:
  • NYTimes news articles:
    • orig source: ldc.upenn.edu
    • N=300000
    • D=102660
  • PubMed abstracts: (PubMed comprises more than 21 million citations for biomedical literature from MEDLINE, life science journals, and online books)
    • orig source: www.pubmed.gov
    • N=8200000
    • D=141043
• Currently I process 1M documents from PubMed with the dimesion 141043, total size of this DataSet on HD is about 250GB.
• To Do: compare the performance using two different LSH families.

Spet 28

• Updated report is here:
  • /nsl/students/dameh/reports/doc/main.pdf
• updated sections :
  • IV. PERFORMANCE METRICS
  • VI. RESULTS

Sept 12

• Comparison Results for H-curve, LSH-Spectral hashing and LSH-Random Projection:
• Metrics used for comparison:
  • FN.SpaceReduction product Metric : Gram matrix level comparison which takes in account both the FN and the space reduction for the approximated gram matrix (as FN decreases space reduction increases)
    • to unify the comparison we set H-curve window_width = DataSets_size/2^k. where k is the number of the LSH hash bits (number of buckets 2^k), in this case we get same matrix size for both H-curve and LSH.
    • this metric gives us the optimal value of k (maximum).
    • LSH-Spectral hashing over performs LSH-Random projection. H-curve over performs both.
    • Graph results can be found at these addresses :
      • FN for different values of k: dameh/reports/dameh/fall11/FN.pdf
      • gram matrix size for different values of k: dameh/reports/dameh/fall11/Size.pdf
      • FN.SpaceReduction : dameh/reports/dameh/fall11/FSprod.pdf
  • Average distance between each point and its Exemplar: which is clustering error metric
    • to unify the comparison we are comparing Avg distance against the number of Exemplars, (we change a parameter in the affinity propagation to control number of Exemplars, or its an initialization parameter for k-means clustering)
    • again, LSH-Spectral hashing over performs LSH-Random projection, and LSH-Spectral hashing over performs H-curve only for smaller number of clusters
    • Graph result can be found at this address: dameh/reports/dameh/fall11/ClusteringError.pdf
  • over all, the advantage of LSH over H-cuve is the parallel property of LSH, where we can get a speedup up to 2^k for the LSH (each node takes care of each bucket)

Summer 2011 (TA)

• Research: Gram Matrix approximation

Aug 22

• fixing bugs in the Hilbert curve and the affinity propagation codes (still working on them)
• revised the metric to be FN.SpaceRedcution product (Inspired from the Energy.Delay product used in the Energy-Efficient Computing )
• cmpt300 Final grading

Aug 8

• working on comparing LSH methods with Space filling Curve methods.
  • for the low level comparing (matrix level using average FN): we get almost same average FN for both spectral hashing and H-curve.
  • comparing in the high level (clustering error):
    • I am picking the optimal value of k for spectral hashing and choosing a widow width for H-curve that gives us same approximated matrix size, using the formula (winWidth=DataSize/2^k)
    • In the affinity propagation, using small value of self similarity (s(i,i)) we get small numbers of clusters and vise verse , so I am changing self similarity values between max and min to get same number of clusters for both methods, which will be the x-axis, on the other hand, the y-axis will be the average distance between each point and its Exemplar.
• PS: I was sick for a week after I came back form China and then busy in setting up and grading assignments for cmpt300.

July 10 - July 24

• Attending workshop on High Performance Computing at National University of Defense Technology, China.

July 8

• Comparing our method using the "Locality Sensitive Hashing" to the method using "Hilbert Space filling Curve"
• Initial results can be found at this address students/dameh/reports/dameh/summer11/H-CurveVSlsh.pdf

Jun 17

• Report should be available here

Jun 03

• Proposed new metric to be used instead of the FN ratio (more powerful to compare different families and to pick the optimal value of k)
• More analysis on the spectral hashing
• Writing up the report with the results to be sent soon

May 19

• Spectral Hashing is Data dependent hashing and more powerful than the random projection, see this comparison examples in the 2D.
• Random Projection Hashing
  • Method:
    • Take random projections of data.
    • Quantize the projections to hash value’s bits.
  • Disadvantage:
    • Data Independent Hashing.
  • Advantage:
    • Strong theoretical guarantees.
    • Simple and fast to compute (linear time).
  • Types:
    • Euclidean distance: two parameters to vary that have the opposite affect (number of random vectors k and quantization width w).
    • L1 distance: uncontrolled, as k increase error increase.
    • Cosine distance: more controlled than L1 as k increase.
• Data-Dependent Hashing
  • Advantage:
    • Data-distribution dependent; powerful than Random Projection.
  • Disadvantage:
    • More complex to compute than Random Projection.
  • Types:
    • Spectral hashing:
      • Compute PCA.
      • Calculate the k smallest single-dimension analytical EigenFunctions.
      • Threshold the analytical EigenFnction of zero to obtain hash value's bits.
    • KernelLSH:
      • The key idea is first embed the data into a lower space using the kernel G and then do LSH in the lower dimensional space.

May 5

• Recap
  • Problem: kernel methods' gram matrix is O(N^2), which is computationally and memory usage infeasible for large-scale datasets
  • Previous Work:
    • Low rank matrix approximation:
      • using EigenDecomposition: Optimal solution
        • Problem: requires O(N^3) computation to construct the low rank matrix
        • Solution: use Nytsrom theorem to approximate the EigenDecomposition of the matrix which requires O(m^2N) where m << N.
        • Problem: In this methods in despite of reducing the computation complexity still we need O(N^2) to store the matrix, so the next methods reduce both computation and memory.
    • Efficient implementation for computing the kernel matrix:
      • Using Spatial Indexing:
        • using Z-order, H-order, kd-tress and other spatial indexing techniques to order the points in the space and then using sliding window where the kernel function computed only between points in that window.
        • Problems: requires O(NlogN) as preprocessing to order the points, fails for very high dimensional Data and hard to parallel
        • Solution: We proposed a method that works fine for large-scale and highly dimensional data that overcomes all pervious works drawbacks, easy to parallel, requires linear time preprocessing and reduce both computation and memory usage down form quadratic to subquadratic:
      • Using Spatial Hashing or Locality sensitive hashing: where the kernel function and the cluster methods works on the buckets individually.
        • LSH families :
          • Random projection:
            • Hamming distance, Euclidean Distance, Cosine Distance
            • Problem for random projection, assume data uniformly distrubuted and it doesn’t take in account if data are in a kernel space, Solution:
          • Spectral Hashing: overcomes previous families problems
          • Kernelized LSH : Problem: more complex to compute
  • In progress: Comparing different LSH families

Spring 2011 (TA/RA)

• Courses:
  • CMPT 885: Special Topics in Computer Architecture
  • CMPT 886: Special Topics in Operating Systems

Fall 2010 (FAS-GF/RA)

• Courses:
  • CMPT 705: Design/Analysis Algorithms
  • CMPT 820: Multimedia Systems

• Worked on Gram matrix approximation, High Performance/large scale application case studies

Summer 2010 (TA)

• Worked on large Scale Kernel methods

Spring 2010 (RA)

• Courses:
  • CMPT 768: Sound Synthesis

• Worked on Locality sensitive hashing families and their applications and implementations