Practical industrial processes usually have nonstationary properties, which make the
monitoring more challenging because the fault information may be buried by nonstationary …

Actual industrial processes often show nonstationary characteristics, so nonstationary
process monitoring is significant to ensure the safety and reliability of industrial processes …

Non-stationary effects are ubiquitous in real world data. In many settings, the observed
signals are a mixture of underlying stationary and non-stationary sources that cannot be …

Low-dimensional representations are key to the success of many video classification
algorithms. However, the commonly-used dimensionality reduction techniques fail to …

Learning under nonstationarity can be achieved by decomposing the data into a subspace
that is stationary and a nonstationary one [stationary subspace analysis (SSA)]. While SSA …

Domain adaptation is a transfer learning approach that has been widely studied in the last
decade. However, existing works still have two limitations: 1) the feature spaces of the …

The sheer amount of high dimensional biomedical data requires machine learning, and
advanced data visualization techniques to make the data understandable for human …

In many real-world applications data exhibits non-stationarity, ie, its distribution changes
over time. One approach to handling non-stationarity is to remove or minimize it before …

We propose a novel algebraic algorithmic framework for dealing with probability
distributions represented by their cumulants such as the mean and covariance matrix. As an …

Domain adaptation can transfer knowledge from the source domain to improve pattern
recognition accuracy in the target domain. However, it is rarely discussed when the target …