Broad Learning System (BLS) that aims to offer an alternative way of learning in deep
structure is proposed in this paper. Deep structure and learning suffer from a time …

Dimensionality reduction has attracted increasing attention, because high-dimensional data
have arisen naturally in numerous domains in recent years. As one popular dimensionality …

Multiview data processing has attracted sustained attention as it can provide more
information for clustering. To integrate this information, one often utilizes the non-negative …

Non-negative matrix factorization (NMF) has attracted sustaining attention in multi-view
clustering, because of its ability of processing high-dimensional data. In order to learn the …

Non-negative matrix factorization (NMF) has recently attracted much attention due to its
good interpretation in perception science and widely applications in various fields. In this …

Non-negative matrix factorization is widely used in multi-view clustering due to its ability of
learning a common dimension-reduced factor. Recently, it is combined with the label …

It is well known that both the label information and the local geometry structure information
are very important for image data clustering and classification. However, nonnegative matrix …

Matrix factorization based multi-view clustering algorithms has attracted much attention in
recent years due to the strong interpretability and efficient implementation. In general, these …

How to learn dimension-reduced representations of image data for clustering has been
attracting much attention. Motivated by that the clustering accuracy is affected by both the …

Hyperspectral unmixing (HSU) is an important technique of remote sensing, which estimates
the fractional abundances and the mixing matrix of endmembers in each mixed pixel from …