Abstract. We propose a novel and constructive algorithm that decomposes an arbitrary tensor into a finite sum of orthonormal rank-1 outer factors. The algorithm, named TTr1SVD, works by converting the tensor into a rank-1 tensor train (TT) series via singular value decomposition (SVD). TTr1SVD naturally generalizes the SVD to the tensor regime and delivers elegant notions of tensor rank and err...

Recommendation Services (RS) are an essential part of online marketing campaigns. They make it possible to automatically suggest advertisements and promotions that fit the interests of individual users. Social networking websites, and the Web 2.0 in general, offer a collaborative online platform where users socialize, interact and discuss topics of interest with each other. These websites have ...

One of the most popular controllers for automatic voltage regulator (AVR) in maintaining level a synchronous generator is fractional-order proportional–integral-derivative (FOPID) controller. Unfortunately, tuning FOPID controller challenging since there are five gains compared to three conventional proportional–integral–derivative (PID) Therefore, this research work presents variant marine pre...

Page Ranking is an important component for information retrieval system. It is used to measure the importance and behavior of web pages. We review two approaches for ranking: HITS concept and Page Rank method. Both approaches focus on the link structure of the Web to find the importance of the Web pages. The Page Rank algorithm calculates the rank of individual web page and Hypertext Induced To...

this paper describes an optimal design for multivariable pi controller with a high gain structure for an irregular system by genetic algorithm. pi controllers with a high gain structure leads to the asymptotic decomposition of the fast and slow modes in the closed loop system that have unique characteristics. the slow modes are asymptotically uncontrollable and unobservable; therefore, they hav...

The matrix rank minimization problem has applications in many fields such as system identification, optimal control, low-dimensional embedding, etc. As this problem is NP-hard in general, its convex relaxation, the nuclear norm minimization problem, is often solved instead. Recently, Ma, Goldfarb and Chen proposed a fixed-point continuation algorithm for solving the nuclear norm minimization pr...

For a given matrix subspace, how can we find a basis that consists of low-rank matrices? This is a generalization of the sparse vector problem. It turns out that when the subspace is spanned by rank-1 matrices, the matrices can be obtained by the tensor CP decomposition. For the higher rank case, the situation is not as straightforward. In this work we present an algorithm based on a greedy pro...

The paper addresses the problem of low-rank trace norm minimization. We propose an algorithm that alternates between fixed-rank optimization and rank-one updates. The fixed-rank optimization is characterized by an efficient factorization that makes the trace norm differentiable in the search space and the computation of duality gap numerically tractable. The search space is nonlinear but is equ...

On April 1, 2007, Alberta became the first province in Canada to introduce cystic fibrosis (CF) to its newborn screening program. The Alberta protocol involves a two-tier algorithm involving an immunoreactive trypsinogen measurement followed by molecular analysis using a CF panel for 39 mutations. Positive screens are followed up with sweat chloride testing and an assessment by a CF specialist....