In recent years, the advances in data collection and statistical analysis promotes canonical
correlation analysis (CCA) available for more advanced research. CCA is the main …

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 …

In affective computing, stress recognition mainly focuses on the relation of stress and
photoelectric information. Researchers have used artificial intelligence to determine stress …

Principal component analysis (PCA) is one of the most fundamental techniques for Big Data
analytics in eg, smart manufacturing and biostatistics, which is capable of extracting the most …

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 …

The constrained composite-convex parameter estimation problem on the networked system,
where the composite-convex function consists of a sum of node-specific smooth loss …

By providing complementary information, multi-modal data is usually helpful for obtaining
good performance in the identification or classification tasks. As an important way to deal …

This paper focuses on unsupervised clustering of hyperspectral pixels whose intensity may
not be available across certain spectral bands. The presence of statistical correlations …

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