Markov fundamental tensor and its applications to network analysis

Random Walk Fundamental Tensor and its Applications to Network Analysis

We first present a comprehensive review of various random walk metrics used in the literature and express them in a consistent framework. We then introduce fundamental tensor – a generalization of the well-known fundamental matrix – and show that classical random walk metrics can be derived from it in a unified manner. We provide a collection of useful relations for random walk metrics that are...

Markerless motion analysis in diffusion tensor fields and its applications

The analysis of deformable objects which have a high-degree of freedom has long been encouraged by numerous researchers because it can be applied to such diverse areas as medical engineering, video surveillance and monitoring, Human Computer Interaction, browsing of video databases, interactive gaming and other growing applications. Within the computerized environments, the systems are largely ...

Trilinear Tensor: The Fundamental Construct of Multiple-view Geometry and Its Applications

The topic of representation, recovery and manipulation of three-dimensional (3D) scenes from two-dimensional (2D) images thereof, provides a fertile ground for both intellectual theoretically inclined questions related to the algebra and geometry of the problem and to practical applications such as Visual Recognition, Animation and View Synthesis, recovery of scene structure and camera ego-moti...

A Network of Hidden Markov Models and Its Analysis

The Structural Classification of Proteins (SCOP) database uses a large number of hidden Markov models (HMMs) to represent families and superfamilies composed of proteins that presumably share the same evolutionary origin. However, how the HMMs are related to one another has not been examined before. In this work, taking into account the processes used to build the HMMs, we propose a working hyp...

Tensor Network States: Algorithms and Applications