This paper describes preliminary research work aimed at obtaining a multi-level iconic representation of 3D objects from geometric meshes. A single-level iconic model describes an object through parts of different dimensions connected to form a hypergraph. The multi-level iconic model, called Non-manifold Multi-Tessellation, incorporates decompositions of an object into parts at different level...

We propose a new couplet of identity and view manifolds for multi-view shape modeling that is applied to automated target tracking and recognition (ATR). The identity manifold captures both inter-class and intra-class variability of target shapes, while a hemispherical view manifold is involved to account for the variability of viewpoints. Combining these two manifolds via a non-linear tensor d...

• An approach based on NMF of multi-network for multi-view clustering is proposed. factorization with multiple regularizations proposed and developed. Extensive experimental studies were performed real data evaluation. Multi-view has attracted increasing attention in recent years since many sets are usually gathered from different sources or described by feature types. Amongst various existing ...

Manifold ranking has been successfully applied in query-oriented multi-document summarization. It not only makes use of the relationships among sentences, but also between given query and sentences. However, information original is often insufficient. So we present a expansion method, which combined manifold to resolve this problem. Our method utilizes term itself knowledge base WordNet expand ...

Multi-channel sparse blind deconvolution, or convolutional coding, refers to the problem of learning an unknown filter by observing its circulant convolutions with multiple input signals that are sparse. This finds numerous applications in signal processing, computer vision, and inverse problems. However, it is challenging learn efficiently due bilinear structure observations respect inputs, as...

Predictive modeling of multiscale and multiphysics systems requires accurate data-driven characterization of the input uncertainties and understanding how they propagate across scales and alter the final solution. We will address three major current limitations in modeling stochastic systems: (1) Most of current uncertainty quantification methods cannot detect and handle discontinuity in the pa...