An unsupervised discriminative extreme learning machine and its applications to data clustering

Extreme Learning Machine (ELM), which was initially proposed for training single-layer feed-forward networks (SLFNs), provides us a unified efficient and effective framework for regression and multiclass classification. Though various ELM variants were proposed in recent years, most of them focused on the supervised learning scenario while little effort was made to extend it into unsupervised learning paradigm. Therefore, it is of great significance to put ELM into learning tasks with only unlabeled data. One popular approach for mining knowledge from unlabeled data is based on the manifold assumption, which exploits the geometrical structure of data by assuming that nearby points will also be close to each other in transformation space. However, considering the manifold information only is insufficient for discriminative tasks. In this paper, we propose an improved unsupervised discriminative ELM (UDELM) model, whose main advantage is to combine the local manifold learning with global discriminative learning together. UDELM can be efficiently optimized by solving a generalized eigen-value decomposition problem. Extensive comparisons over several state-of-the-art models on clustering image and emotional EEG data demonstrate the efficacy of UDELM.