Sparse principal component analysis (PCA) imposes extra constraints or penalty terms to the standard PCA to achieve sparsity. In this paper, we first introduce an efficient algor...
We propose a novel clustering scheme for spatio-temporal segmentation of sparse motion fields obtained from feature tracking. The approach allows for the segmentation of meaningfu...
Interoperability problems arise when complex software systems are constructed by integrating distinct, and often heterogeneous, components. By performing interoperability analysis...
Principal curves, like principal components, are a tool used in multivariate analysis for ends like feature extraction. Defined in their original form, principal curves need not ex...
The deployment of software components frequently fails because dependencies on other components are not declared explicitly or are declared imprecisely. This results in an incompl...