Link prediction in graphs is studied by modeling the dyadic interactions among two nodes. The relationships can be more complex than simple and could require user to model super-dyadic associations Such modeled using a hypergraph, which generalization of graph where hyperedge connect In this work, we consider problem k-uniform hypergraph. We utilize tensor-based representation hypergraphs propo...

We introduce a nonsymmetric, associative tensor product among representations of Cuntz algebras by using embeddings. We show the decomposition formulae of tensor products for permutative representations explicitly We apply decomposition formulae to determine properties of endomorphisms. Mathematics Subject Classifications (2000). 47L55, 81T05.

We consider the problem of learning associative mixtures for classification and regression problems, where the output is modeled as a mixture of conditional distributions, conditioned on the input. In contrast to approaches such as expectation maximization (EM) or variational Bayes, which can get stuck in bad local optima, we present a tensor decomposition method which is guaranteed to correctl...

Tucker decomposition is a standard method for processing multi-way (tensor) measurements and finds many applications in machine learning data mining, among other fields. When tensor arrive streaming fashion or are too to jointly decompose, incremental analysis preferred. In addition, dynamic adaptation of bases desired when the nominal subspaces change. At same time, it has been documented that...

Tensor decomposition is a powerful computational tool for multiway data analysis. Many popular tensor decomposition approaches—such as the Tucker decomposition and CANDECOMP/PARAFAC (CP)—amount to multi-linear factorization. They are insufficient to model (i) complex interactions between data entities, (ii) various data types (e.g.missing data and binary data), and (iii) noisy observations and ...

This study explores an approach for analysing the mirror (reflective) symmetry of 3D shapes with tensor based sparse decomposition. The approach combines non-negative tensor decomposition and directional texture synthesis, with symmetry information about 3D shapes that is represented by 2D textures synthesised from sparse, decomposed images. This technique requires the center of mass of 3D obje...

This article combines a tutorial on state-of-art tensor decomposition as it relates to big data analytics, with original research on parallel and distributed computation of low-rank decomposition for big tensors, and a concise primer on Hadoop-MapReduce. A novel architecture for parallel and distributed computation of low-rank tensor decomposition that is especially well-suited for big tensors ...

Modeling inverse dynamics is crucial for accurate feedforward robot control. The model computes the necessary joint torques, to perform a desired movement. The highly non-linear inverse function of the dynamical system can be approximated using regression techniques. We propose as regression method a tensor decomposition model that exploits the inherent threeway interaction of positions × veloc...

In this paper, a successive supersymmetric rank-1 decomposition of a real higher-order supersymmetric tensor is considered. To obtain such a decomposition, we design a greedy method based on iteratively computing the best supersymmetric rank-1 approximation of the residual tensors. We further show that a supersymmetric canonical decomposition could be obtained when the method is applied to an o...