Clustering is a machine learning paradigm of dividing sample subjects into a number of
groups such that subjects in the same groups are more similar to those in other groups. With …

For many computer vision applications, the data sets distribute on certain low-dimensional
subspaces. Subspace clustering is to find such underlying subspaces and cluster the data …

Digital photographs and visual data have become increasingly available, especially on the
Web considered as the largest image database to date. However, the value of multimedia …

Multi-view clustering has attracted increasing attention in multimedia, machine learning and
data mining communities. As one kind of the essential multi-view clustering algorithm, multi …

In past decade, more and more data are collected from multiple sources or represented by
multiple views, where different views describe distinct perspectives of the data. Although …

Unsupervised feature selection chooses a subset of discriminative features to reduce feature
dimension under the unsupervised learning paradigm. Although lots of efforts have been …

The objective of this study is to process the soil images to generate a digital soil
classification system for rural farmers at low cost. Soil texture is the main factor to be …

In this paper, we investigate the specific emitter identification (SEI) problem, which
distinguishes different emitters using features generated by the nonlinearity of the power …

Similarity matrix is of vital importance for graph-based multi-view clustering models, which
can depict the nonlinear structure information among samples. However, most existing …

One underlying assumption of the conventional multi-view learning algorithms is that all
examples can be successfully observed on all the views. However, due to various failures or …