Data collection within a real-world environment may be compromised by several factors such as data-logger malfunctions and communication errors, during which no data is collected. As consequence, appropriate tools are required to handle the missing values when analysing processing data. This problem often tackled via matrix decomposition. While it has been successfully applied in wide range of ...

Matrix data sets arise in network analysis for medical applications, where each belongs to a subject and represents measurable phenotype. These large dimensional are often modeled using lower-dimensional latent variables, which explain most of the observed variability can be used predictive purposes. In this paper, we provide asymptotic convergence guarantees estimation hierarchical statistical...

Amodified Karhunen-Loève Decomposition/Proper Orthogonal Decomposition method, named Smooth Decomposition (SD) (also named smooth Karhunen-Loève decomposition), was recently introduced to analyze stationary random signal. It is based on a generalized eigenproblem defined from the covariance matrix of the random process and the covariance matrix of the associated time-derivative random process. ...

In this paper we consider the problem of decomposing an integer matrix into a weighted sum of binary matrices that have the strict consecutive ones property. This problem is motivated by an application in cancer radiotherapy planning, namely the sequencing of multileaf collimators to realize a given intensity matrix. In addition we also mention another application in the design of public transp...

A new methodology is developed for joint analysis of a matrix and accompanying documents, with the documents associated with the matrix rows/columns. The documents are modeled with a focused topic model, inferring interpretable latent binary features for each document. A new matrix decomposition is developed, with latent binary features associated with the rows/columns, and with imposition of a...

In this paper, we present a new graph model of sparse matrix decomposition for parallel sparse matrix–vector multiplication. Our model differs from previous graph-based approaches in two main respects. Firstly, our model is based on edge colouring rather than vertex partitioning. Secondly, our model is able to correctly quantify and minimise the total communication volume of the parallel sparse...