Rajendra Kumar Roul
ABSTRACT
Based on the architecture of deep learning, Multilayer Extreme Learning Machine (ML-ELM) has many good characteristics which make it distinct and widespread classifier in the domain of text mining. Some of its salient features include non-linear mapping of features into a high dimensional space, high level of data abstraction, no backpropagation, higher rate of learning etc. This paper studies the importance of ML-ELM feature space and tested the performance of various traditional clustering techniques on this feature space. Empirical results show the efficiency and effectiveness of the feature space of ML-ELM compared to TF-IDF vector space which justifies the prominence of deep learning.
- A. K. Jain, M. N. Murty, and P. J. Flynn, "Data clustering: a review," ACM computing surveys (CSUR), vol. 31, no. 3, pp. 264--323, 1999. Google Scholar
Digital Library
- S. Liang, E. Yilmaz, and E. Kanoulas, "Dynamic clustering of streaming short documents," in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 995--1004, ACM, 2016. Google ScholarDigital Library
- J. Yin and J. Wang, "A dirichlet multinomial mixture model-based approach for short text clustering," in Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 233--242, ACM, 2014. Google ScholarDigital Library
- R. K. Roul, S. Gugnani, and S. M. Kalpeshbhai, "Clustering based feature selection using extreme learning machines for text classification," in India Conference (INDICON), 2015 Annual IEEE, pp. 1--6, IEEE, 2015.Google Scholar
- R. K. Roul, A. Bhalla, and A. Srivastava, "Commonality-rarity score computation: A novel feature selection technique using extended feature space of elm for text classification," in Proceedings of the 8th annual meeting of the Forum on Information Retrieval Evaluation, pp. 37--41, ACM, 2016. Google ScholarDigital Library
- R. K. Roul and P. Rai, "A new feature selection technique combined with elm feature space for text classification," in 13th International Conference on Natural Language Processing, pp. 285--292, 2016.Google Scholar
- Z. L. Z. W. Da JIAO and L. Cheng, "Kernel clustering algorithm," Chinese Journal of Computers, vol. 6, p. 004, 2002.Google Scholar
- G.-B. Huang, X. Ding, and H. Zhou, "Optimization method based extreme learning machine for classification," Neurocomputing, vol. 74, no. 1, pp. 155--163, 2010. Google ScholarDigital Library
- L. L. C. Kasun, H. Zhou, G.-B. Huang, and C. M. Vong, "Representational learning with extreme learning machine for big data," IEEE Intelligent Systems, vol. 28, no. 6, pp. 31--34, 2013.Google Scholar
- G.-B. Huang and L. Chen, "Enhanced random search based incremental extreme learning machine," Neurocomputing, vol. 71, no. 16, pp. 3460--3468, 2008. Google ScholarDigital Library
- G.-B. Huang, Q.-Y. Zhu, and C.-K. Siew, "Extreme learning machine: theory and applications," Neurocomputing, vol. 70, no. 1, pp. 489--501, 2006.Google ScholarCross Ref
- G.-B. Huang, H. Zhou, X. Ding, and R. Zhang, "Extreme learning machine for regression and multiclass classification," IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 42, no. 2, pp. 513--529, 2012. Google ScholarDigital Library
- G.-B. Huang, L. Chen, C. K. Siew, et al., "Universal approximation using incremental constructive feedforward networks with random hidden nodes," IEEE Transactions on Neural Networks, vol. 17, no. 4, pp. 879--892, 2006. Google ScholarDigital Library
- G.-B. Huang and L. Chen, "Convex incremental extreme learning machine," Neurocomputing, vol. 70, no. 16, pp. 3056--3062, 2007. Google ScholarDigital Library
- J. A. Hartigan and M. A. Wong, "Algorithm as 136: A k-means clustering algorithm," Journal of the Royal Statistical Society. Series C (Applied Statistics), vol. 28, no. 1, pp. 100--108, 1979.Google ScholarCross Ref
- M. Ester, H.-P. Kriegel, J. Sander, X. Xu, et al., "A density-based algorithm for discovering clusters in large spatial databases with noise.," in Kdd, vol. 96, pp. 226--231, 1996. Google ScholarDigital Library
- D. Defays, "An efficient algorithm for a complete link method," The Computer Journal, vol. 20, no. 4, pp. 364--366, 1977.Google ScholarCross Ref
- J. C. Bezdek, R. Ehrlich, and W. Full, "Fcm: The fuzzy c-means clustering algorithm," Computers & Geosciences, vol. 10, no. 2, pp. 191--203, 1984.Google ScholarCross Ref
Recommendations
Multilayer extreme learning machine: a systematic review
AbstractMajority of the learning algorithms used for the training of feedforward neural networks (FNNs), such as backpropagation (BP), conjugate gradient method, etc. rely on the traditional gradient method. Such algorithms have a few drawbacks, including ...
Impact of multilayer ELM feature mapping technique on supervised and semi-supervised learning algorithms
AbstractThe popularity of deep learning architecture is increasing day by day. But the majority of deep learning algorithms have their own limitations, such as slow convergence, high training time, sensitivity to noisy data, the problem of local minimum, ...
Study and Understanding the Significance of Multilayer-ELM Feature Space
Big Data AnalyticsAbstractMulti-layer Extreme Learning Machine (Multi-layer ELM) is one of the most popular deep learning classifiers among other traditional classifiers because of its good characteristics such as being able to manage a huge volume of data, no ...
Comments