Neeraj Sharma

Research Work & Summary

  1. N. Sharma, K. Masalia, "Analysis on Complementary Count Min Sketch," (Independent Study Report.pdf)
    • Proposed and analyzed a novel data structure called Complementary Count Min Sketch (CCMS) to support deletion in count min sketch.
    • Compared the performance of CCMS with different sketches for feature selection in high dimensional data settings.
    • Received comparable performance with Count Sketch under Power and Zipf's law distribution settings.
  2. N. Sharma, A. D. Dileep, and V. Thenkanidiyoor, "Text Classification using Hierarchical Sparse Representation Classifiers," 16th IEEE International Conference on Machine Learning and Applications (ICMLA), Cancun, 2017, pp. 1015-1019. (paper_2.pdf)
    • Built a Hierarchical Sparse Representation Classifier (HSRC) and explored it for text classification.
    • Explored weighted decomposition principal component analysis (WDPCA) technique to highlight the middle principal components to construct the dictionary that emphasizes discrimination among confusing classes.
    • HSRC classified 83.30% of the documents correctly opposed to the 78.78% (using SRC) with WDPCA on 20 Newsgroup corpus.
  3. N. Sharma, A. Sharma, V. Thenkanidiyoor, and A. D. Dileep, "Text classification using combined sparse representation classifiers and support vector machines," 4th International Symposium on Computational and Business Intelligence (ISCBI), Olten, 2016, pp. 181-185. (paper_1.pdf)
    • Explored frequency based kernels such as Histogram intersection kernel, Χ2-kernel and Hellinger’s kernel for text classification using SVMs.
    • Combined the SRC and SVM classifiers based on voting scheme. Obtained 81.83% accuracy using on 20 Newsgroup corpus.