Extreme Learning Machine (ELM) is a neural network architecture in which hidden layer weights are randomly chosen and output layer weights determined analytically. We interpret ELM as an approximation to a network with infinite number of hidden units. The operation of the infinite network is captured by neural network kernel (NNK). We compare ELM and NNK both as part of a kernel method and in n...

Increasing the scalability of machine learning to handle big volume of data is a challenging task. The scale up approach has some limitations. In this paper, we proposed a scale out approach for CNN-ELM based on MapReduce on classifier level. Map process is the CNN-ELM training for certain partition of data. It involves many CNN-ELM models that can be trained asynchronously. Reduce process is t...

Hyperspectral image processing algorithms are computationally very costly, which makes them good candidates for parallel and, specifically, GPU processing. Extreme Learning Machine (ELM) is a recently proposed classification algorithm very suitable for its implementation on GPU platforms. In this paper we propose an efficient GPU implementation of an ELM-based classification strategy for hypers...

Local Coupled Extreme Learning Machine (LCELM) is a recently-proposed variant of ELM, which assigns an address for each hidden-layer node and activates the hidden-layer node when its activated degree is less than a given threshold. In this paper, an improved version of LCELM is proposed by developing a new way to initialize the address for each hidden-layer node and calculating the activated de...

Discriminative clustering is an unsupervised learning framework which introduces the discriminative learning rule of supervised classification into clustering. The underlying assumption is that a good partition (clustering) of the data should yield high discrimination, namely, the partitioned data can be easily classified by some classification algorithms. In this paper, we propose three discri...

This paper proposes a method which is the advanced modification of the original Extreme Learning Machine with a new tool to solve the missing data problem. It uses a cascade of L1 penalty (LARS) and L2 penalty (Tikhonov regularization) on ELM to regularize the matrix computations and hence make the MSE computation more reliable, and on the other hand, it estimates the expected pairwise distance...

A challenge in big data classification is the design of highly parallelized learning algorithms. One solution to this problem is applying parallel computation to different components of a learning model. In this paper, we first propose an extreme learning machine tree (ELM-Tree) model based on the heuristics of uncertainty reduction. In the ELM-Tree model, information entropy and ambiguity are ...

We propose a fast method for 3D shape segmentation and labeling via Extreme Learning Machine (ELM). Given a set of example shapes with labeled segmentation, we train an ELM classifier and use it to produce initial segmentation for test shapes. Based on the initial segmentation, we compute the final smooth segmentation through a graph-cut optimization constrained by the super-face boundaries obt...

This paper presents the Optimally-Pruned Extreme Learning Machine (OP-ELM) toolbox. This novel, fast and accurate methodology is applied to several regression and classification problems. The results are compared with widely known Multilayer Perceptron (MLP) and Least-Squares Support Vector Machine (LS-SVM) methods. As the experiments (regression and classification) demonstrate, the OP-ELM meth...

Extreme Learning Machine, ELM, is a newly available learning algorithm for single layer feedforward neural networks (SLFNs), and it has proved to show the best compromise between learning speed and accuracy of the estimations. In this paper, a methodology based on Optimal-Pruned ELM (OP-ELM) for function approximation enhanced with variable selection using the Delta Test is introduced. The leas...