Algorithm Method Advantage Challenge
Extreme Learning Machine (ELM) [28] Reviews the theoretical model of different ELM algorithms High stability, speed, and accuracy under general or specific conditions Supports many learning applications, such as regression, classification, feature selection, clustering, and representational learning Improves a data-dependent generalization mechanism for generating hidden-layer parameters
Artificial Neural Networks (ANNs) [29] Demonstrate LM–ANN models to analyze and predict the output given by a data set Compare different learning strategies Generalization and optimization capabilities of the learning system
Online machine learning [30] Online NN, Online Support Vector Machine (SVM), online Kernel Principal Component Analysis (KPCA) The classifier can adapt or retrain the changes in the input data for prediction. The prediction and online classification processes are sometimes integrated for big data analytics. An extensive demonstration in practical applications remains a significant challenge for online-learning methods.
ELM Clustering (ELMC) [31] KMeans algorithm in ELM, non-negative matrix factorization (NMF) algorithm in ELM An ELM feature space is supported by KMeans clustering. It can handle a large number of input parameters. NMF is tested for finding the low-dimensional representation of non-negative high-dimensional data, which can provide initiative data mapping to simplify the process. The overall performance has less effect with the changes in the hidden-layer nodes. Further testing needs to be conducted, especially with other ELM feature-mapping techniques.
Unsupervised Discriminative Extreme Learning Machine (UDELM) [32] Handles learning tasks with only unlabeled data Merges the local manifold learning with global discriminative learning Gives better data representation than the ordinary unsupervised ELM, which conserves only the local structure of data Generalization and optimization factors need to be enhanced.
Table 1: Recent development in the implementation of LM algorithms for big data analytics.