Jianping Yin

TL;DR: This special issue includes eight original works that detail the further developments of ELMs in theories, applications, and hardware implementation.

TL;DR: A convolutional autoencoders structure is developed to learn embedded features in an end-to-end way and a clustering oriented loss is directly built on embedded features to jointly perform feature refinement and cluster assignment.

TL;DR: This paper proposes a global and local structure preservation framework for feature selection (GLSPFS) which integrates both global pairwise sample similarity and local geometric data structure to conduct feature selection and shows that the best feature selection performance is always obtained when the two factors are appropriately integrated.

TL;DR: A general learning framework, termed multiple kernel extreme learning machines (MK-ELM), to address the lack of a general framework for ELM to integrate multiple heterogeneous data sources for classification and can achieve comparable or even better classification performance than state-of-the-art MKL algorithms, while incurring much less computational cost.

TL;DR: A Radiating Gradient Vector Flow (RGVF) aiming at accurate extraction of both the nucleus and cytoplasm from a single-cell cervical smear image is proposed, and is thus robust to contaminations and can effectively locate the obscure boundaries.