Big multimedia data is heterogeneous in essence, that is, the data may be a mixture of
video, audio, text, and images. This is due to the prevalence of novel applications in recent …

In this paper, we review recent progresses in the area of mining data from multiple data
sources. The advancement of information communication technology has generated a large …

k nearest neighbor (kNN) method is a popular classification method in data mining and
statistics because of its simple implementation and significant classification performance …

The blooming proliferation of aeronautics and astronautics platforms, together with the ever-
increasing remote sensing imaging sensors on these platforms, has led to the formation of …

K nearest neighbors (kNN) is an efficient lazy learning algorithm and has successfully been
developed in real applications. It is natural to scale the kNN method to the large scale …

Previous multi-view spectral clustering methods are a two-step strategy, which first learns a
fixed common representation (or common affinity matrix) of all the views from original data …

Existing video hash functions are built on three isolated stages: frame pooling, relaxed
learning, and binarization, which have not adequately explored the temporal order of video …

Human activity recognition in videos with convolutional neural network (CNN) features has
received increasing attention in multimedia understanding. Taking videos as a sequence of …

In this paper, we propose a new unsupervised spectral feature selection model by
embedding a graph regularizer into the framework of joint sparse regression for preserving …

Traditional graph clustering methods consist of two sequential steps, ie, constructing an
affinity matrix from the original data and then performing spectral clustering on the resulting …