Unsupervised Extreme Learning Machine Research Articles

NMR image segmentation based on Unsupervised Extreme Learning Machine

NMR image segmentation based on Unsupervised Extreme Learning Machine

Unsupervised extreme learning machine with representational features

Extreme learning machine (ELM) is not only an effective classifier but also a useful cluster. Unsupervised extreme learning machine (US-ELM) gives favorable performance compared to state-of-the-art clustering algorithms. Extreme learning machine as an auto encoder (ELM-AE) can obtain principal components which represent original samples. The proposed unsupervised extreme learning machine based on embedded features of ELM-AE (US-EF-ELM) algorithm applies ELM-AE to US-ELM. US-EF-ELM regards embedded features of ELM-AE as the outputs of US-ELM hidden layer, and uses US-ELM to obtain the embedded matrix of US-ELM. US-EF-ELM can handle the multi-cluster clustering. The learning capability and computational efficiency of US-EF-ELM are as same as US-ELM. By experiments on UCI data sets, we compared US-EF-ELM k-means algorithm with k-means algorithm, spectral clustering algorithm, and US-ELM k-means algorithm in accuracy and efficiency.

On the Distributed Implementation of Unsupervised Extreme Learning Machines for Big Data

Abstract The emergence of the big data problem has pushed the machine learning research community to develop unsupervised, distributed and computationally efficient learning algorithms to benefit from this data. Extreme learning machines (ELM) have gained popularity as a neuron based architecture with fast training time and good generalization. In this work, we parallelize an ELM algorithm for unsupervised learning on a distributed framework to learn clustering models from big data based on the unsupervised ELM algorithm proposed in the literature. We propose three approaches to do so: 1) Parallel US-ELM which simply distributes the data over computing nodes, 2) Hierarchical US-ELM which hierarchically clusters the data and 3) Ensemble US- ELM which is an ensemble of weak ELM models. The algorithms achieved faster training times compared to their serial counterparts and generalized better than other clustering algorithms in the literature, when tested on multiple datasets from UCI.

Semi-supervised and unsupervised extreme learning machines.

Extreme learning machines (ELMs) have proven to be efficient and effective learning mechanisms for pattern classification and regression. However, ELMs are primarily applied to supervised learning problems. Only a few existing research papers have used ELMs to explore unlabeled data. In this paper, we extend ELMs for both semi-supervised and unsupervised tasks based on the manifold regularization, thus greatly expanding the applicability of ELMs. The key advantages of the proposed algorithms are as follows: 1) both the semi-supervised ELM (SS-ELM) and the unsupervised ELM (US-ELM) exhibit learning capability and computational efficiency of ELMs; 2) both algorithms naturally handle multiclass classification or multicluster clustering; and 3) both algorithms are inductive and can handle unseen data at test time directly. Moreover, it is shown in this paper that all the supervised, semi-supervised, and unsupervised ELMs can actually be put into a unified framework. This provides new perspectives for understanding the mechanism of random feature mapping, which is the key concept in ELM theory. Empirical study on a wide range of data sets demonstrates that the proposed algorithms are competitive with the state-of-the-art semi-supervised or unsupervised learning algorithms in terms of accuracy and efficiency.