Item-Item Collaborative Filtering via Graph Regularization

A recommendation algorithm aims to predict the quality of a user's future interaction with certain items based on their previous interactions. As research progresses, these algorithms are becoming increasingly more complicated with the use of machine learning and neural networks. This paper looks into a more simple solution. The recommendation...

Three-dimensional displacement fields from insar through tikhonov regularization and least-squares variance component estimation

Synthetic aperture radar interferometry (InSAR) measures the projection of three-dimensional (3D) ground displacement in the range direction and in the azimuth direction through image processing. The incapability of InSAR in measuring the 3D displacements restricts its capability for assessing real Earth surface deformation. The near-polar...

Estimation and reduction of random noise in mass anomaly time-series from satellite gravity data by minimization of month-to-month year-to-year double differences

We propose a technique to regularize a GRACE-based mass-anomaly time-series in order to (i) quantify the Standard Deviation (SD) of random noise in the data, and (ii) reduce the level of that noise. The proposed regularization functional minimizes the Month-to-month Year-to-year Double Differences (MYDD) of mass anomalies. As such, it does...