Multi-view data with each view corresponding to a type of feature set are common in real world. Usually, previous multi-view learning methods assume complete views. However, multi-view data are often incomplete, namely some samples have incomplete feature sets. Besides, most data are unlabeled due to a large cost of manual annotation, which makes learning of such data a challenging problem. In ...

Marine industry requires continued development of new technologies in order to produce oil. An essential requirement in design is to be able to compare experimental data from prototype structures with predicted information from a corresponding analytical finite element model. In this study, structural model updating may be defined as the fit of an existing analytical model in the light of measu...

In hyperspectral images, once the pure spectra of the materials are known, hyperspectral unmixing seeks to find their relative abundances throughout the scene. We present a novel variational model for hyperspectral unmixing from incomplete noisy data, which combines a spatial regularity prior with the knowledge of the pure spectra. The material abundances are found by minimizing the resulting c...

marine industry requires continued development of new technologies in order to produce oil. an essential requirement in design is to be able to compare experimental data from prototype structures with predicted information from a corresponding analytical finite element model. in this study, structural model updating may be defined as the fit of an existing analytical model in the light of measu...

The availability of empirical data that capture the structure and behavior complex networked systems has been greatly increased in recent years, however a versatile computational toolbox for unveiling system's nodal interaction dynamics from remains elusive. Here we develop two-phase approach autonomous inference network dynamics, its effectiveness is demonstrated by tests inferring neuronal, g...

Nowadays multi-modal visual data are much easier to access as the technology develops. Nevertheless, there is an underlying problem hidden behind the emerging multi-modality techniques: What if one/more modal data fail? Motivated by this question, we propose an unsupervised method which well handles the incomplete multi-modal data by transforming the original and incomplete data to a new and co...