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Study and Understanding the Significance of Multilayer-ELM Feature Space

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Big Data Analytics (BDA 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12581))

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Abstract

Multi-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 backpropagation, faster learning speed, maximum level of data abstraction etc. Another distinct feature of Multi-layer ELM is that it can be able to make the input features linearly separable by mapping them non-linearly to an extended feature space. This architecture shows acceptable performance as compared to other deep networks. The paper studies the high dimensional feature space of Multi-layer ELM named as MLELM-HDFS in detail by performing different conventional unsupervised and semi-supervised clustering techniques on it using text data and comparing it with the traditional TF-IDF vector space named as TFIDF-VS in order to show its importance. Results on both unsupervised and semi-supervised clustering techniques show that MLELM-HDFS is more promising than the TFIDF-VS.

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Notes

  1. 1.

    https://pythonprogramming.net/lemmatizing-nltk-tutorial/.

  2. 2.

    https://www.nltk.org/.

  3. 3.

    https://github.com/alvations/pywsd.

  4. 4.

    https://www.nltk.org/.

  5. 5.

    http://www.dataminingresearch.com/index.php/2010/09/classic3-classic4-datasets/.

  6. 6.

    http://qwone.com/~jason/20Newsgroups/.

  7. 7.

    http://www.daviddlewis.com/resources/testcollections/reuters21578/.

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Author information

Authors and Affiliations

  1. Department of Computer Science, Thapar Institute of Engineering and Technology, Patiala, 147004, Punjab, India

    Rajendra Kumar Roul

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  1. Rajendra Kumar Roul
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Correspondence to Rajendra Kumar Roul .

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Editors and Affiliations

  1. ISAE-ENSMA, Chasseneuil, France

    Ladjel Bellatreche

  2. Indraprastha Institute of Information Technology, New Delhi, India

    Vikram Goyal

  3. Iwate Prefectural University, Takizawa, Japan

    Hamido Fujita

  4. Ashoka University, Sonepat, India

    Anirban Mondal

  5. IIIT Hyderabad, Hyderabad, India

    P. Krishna Reddy

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Roul, R.K. (2020). Study and Understanding the Significance of Multilayer-ELM Feature Space. In: Bellatreche, L., Goyal, V., Fujita, H., Mondal, A., Reddy, P.K. (eds) Big Data Analytics. BDA 2020. Lecture Notes in Computer Science(), vol 12581. Springer, Cham. https://doi.org/10.1007/978-3-030-66665-1_3

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  • DOI: https://doi.org/10.1007/978-3-030-66665-1_3

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  • Online ISBN: 978-3-030-66665-1

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