… In this study, a supervised probabilistic slow feature analysis (SPSFA) is proposed to assess
… First, the slow features (SFs) are modeled in state–space form as a serial correlation feature …

… Recently, a new multivariate statistical analysis approach named slow feature analysis (SFA)
has been introduced into the process monitoring field [23], [24]. This method can not only …

… In this article, based on the slow feature analysis (SFA) theory and the nonsubsampled
contourlet transform (NSCT) algorithm, we propose a novel unsupervised change detection …

Slow feature analysis (SFA) is an efficient technique in exploring process dynamic information
and is suitable for quality‐relevant process monitoring. However, involving quality‐…

… In this work, a novel high-order tensor slow feature analysis model is proposed to handle the
3-D and dynamical issues simultaneously. The proposed model can be solved by iteratively …

… data analysis algorithm called slow feature analysis (SFA) [ … in extracting independent slowly
varying slow features (SFs) … be characterized by the extracted slowly varying SFs. That is to …

… We present and test an extension of slow feature analysis as a novel approach to nonlinear
… The algorithm is based on a mathematical analysis of slow feature analysis for the case of …

… Eventually, a novel nonlinear slow feature analysis algorithm, namely, locally weighted slow
feature analysis, is put forward to model the time variance and nonlinearity of this process. …

… study proposes a globallocal slow-feature-analysis-based convolutional … slow feature sg,
a global dynamic slow feature sg, a local static slow feature sl and a local dynamic slow feature …

… motion features. Our method is based on: 1) Slow feature analysis principle from which motion
features … 2) Integration of the local motion feature into a global classification architecture. …