Impact of multilayer ELM feature mapping technique on supervised and semi-supervised learning algorithms

The 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, etc. A deep learning architecture called Multilayer ELM can overcome these limitations to a large extent where there is no backpropagation and hence saves a lot of training time, eliminates the need to fine-tune the parameters, ensure global optimum, able to handle a large volume of data, etc. As above, the most important feature of Multilayer ELM is the characteristics of its feature space, where the input features can linearly be separable without using any kernel techniques, and it still has not taken any attention from the research community. The paper studies the feature space of Multilayer ELM by considering its architecture and feature mapping technique. To justify the effectiveness of its feature mapping technique, semi-supervised and supervised learning algorithms are tested extensively on the feature space of Multilayer ELM and on TF-IDF vector space. Results from the experiment have shown that Multilayer ELM feature space is more effective than the TF-IDF vector space, and the performance of Multilayer ELM is better compared to the baseline machine and deep learning architectures.