In this paper, we present a novel algorithm for incremental principal component analysis. Based on the LargestEigenvalue-Theory, i.e. the eigenvector associated with the largest ei...
In this paper, a novel subspace method called diagonal principal component analysis (DiaPCA) is proposed for face recognition. In contrast to standard PCA, DiaPCA directly seeks t...
Parameterized Appearance Models (PAMs) (e.g. eigentracking, active appearance models, morphable models) use Principal Component Analysis (PCA) to model the shape and appearance of...
The relationship between apparently disparate sets of data is a critical component of interpreting materials' behavior, especially in terms of assessing the impact of the mic...
We present a method for simultaneous dimension reduction and metastability analysis of high dimensional time series. The approach is based on the combination of hidden Markov model...
Illia Horenko, Johannes Schmidt-Ehrenberg, Christo...