Corpora for topic detection and tracking
Topic detection and tracking
Document clustering based on non-negative matrix factorization
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
A Novel Kernel Method for Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
A tutorial on spectral clustering
Statistics and Computing
Letters: Convex incremental extreme learning machine
Neurocomputing
K-means clustering versus validation measures: a data-distribution perspective
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on cybernetics and cognitive informatics
An efficient nonnegative matrix factorization approach in flexible kernel space
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Extreme support vector machine classifier
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
An approach to find embedded clusters using density based techniques
ICDCIT'05 Proceedings of the Second international conference on Distributed Computing and Internet Technology
Least squares quantization in PCM
IEEE Transactions on Information Theory
Mercer kernel-based clustering in feature space
IEEE Transactions on Neural Networks
Universal approximation using incremental constructive feedforward networks with random hidden nodes
IEEE Transactions on Neural Networks
A Fast and Accurate Online Sequential Learning Algorithm for Feedforward Networks
IEEE Transactions on Neural Networks
Nonnegative Matrix Factorization in Polynomial Feature Space
IEEE Transactions on Neural Networks
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Extreme learning machine (ELM), used for the ''generalized'' single-hidden-layer feedforward networks (SLFNs), is a unified learning platform that can use a widespread type of feature mappings. In theory, ELM can approximate any target continuous function and classify any disjoint regions; in application, many experiment results have already demonstrated the good performance of ELM. In view of the good properties of the ELM feature mapping, the clustering problem using ELM feature mapping techniques is studied in this paper. Experiments show that the proposed ELM kMeans algorithm and ELM NMF (nonnegative matrix factorization) clustering can get better clustering results than the corresponding Mercer kernel based methods and the traditional algorithms using the original data. Moreover, the proposed methods have the advantage of being more convenient to implementation and computation, as the ELM feature mapping is much simpler than the Mercer kernel function based feature mapping methods.