Sensor networks, as a promising network paradigm, have been widely applied in a great
deal of critical real-world applications. A key challenge in sensor networks is how to improve …

Canonical correlation analysis (CCA) is a powerful technique for discovering whether or not
hidden sources are commonly present in two (or more) datasets. Its well-appreciated merits …

A novel online joint kernel learning and clustering (OKC) framework is derived which is
capable of determining time-varying clustering configurations without the need for training …

Segmentation of sequential sensor data streams and classification of each segment are
common steps in tasks dealing with the detection of events of interest in such data. In this …

A novel optimization framework for joint unsupervised clustering and kernel learning is
derived. Sparse nonnegative matrix factorization of kernel covariance matrices is utilized to …

Principal component analysis (PCA) has well-documented merits for data extraction and
dimensionality reduction. PCA deals with a single dataset at a time, and it is challenged …

In this letter, we discuss the problem of unsupervised clustering of sensor signals based on
their information content. In the past, the problem has been formulated as a matrix …

Successful clustering of multiple objects using kernels, heavily relies on the proper selection
of kernel parameters. This can be a computationally complex process and may necessitate …

Due to the rapid development of the wireless communication network, the total amount of
data in the future is expected to triple. In the next decade, its total will grow by a factor of …

This work discusses the problem of unsupervised clustering of signals/data vectors based
on their information content. A correlation based perspective to the clustering problem has …