Optimization on Riemannian manifolds-the result of smooth geometry and optimization
merging into one elegant modern framework-spans many areas of science and engineering …

The aim of this paper is twofold. First, we show that a certain concatenation of a proximity
operator with an affine operator is again a proximity operator on a suitable Hilbert space …

Despite the rapid development of computational hardware, the treatment of large and high
dimensional data sets is still a challenging problem. This paper provides a twofold …

The dominant eigenvector of the covariance matrix of a zero-mean data distribution
describes the line wherein the variance of the projected data is maximized. In practical …

Currently, many robust principal component analysis (PCA) methods with low-dimensional
representations have been proposed to improve the overall performance of image …

The $\ell_p $ subspace approximation problem is an NP-hard low rank approximation
problem that generalizes the median hyperplane problem ($ p= 1$), principal component …

Remote sensing systems offer an increased opportunity to record multi-temporal and
multidimensional images of the earth's surface. This opportunity greatly increases the …

Remote sensing systems offer an increased opportunity to record multi-temporal and
multidimensional images of the earth's surface. This opportunity greatly increases the …

Principal Component Analysis (PCA) is a popular tool for dimension reduction and feature
extraction in data analysis. Probabilistic PCA (PPCA) extends the standard PCA by using a …

Principal component analysis (PCA) is known to be sensitive to outliers, so that various
robust PCA variants were proposed in the literature. A recent model, called reaper, aims to …