Color correction involves mapping device RGBs to display counterparts or to corresponding XYZs. A popular methodology is to take an image of a color chart and then solve for the best 33 3 matrix that maps the RGBs to the corresponding known XYZs. However, this approach fails at times when the intensity of the light varies across the chart. This variation needs to be removed before estimating th...

Tensor CANDECOMP/PARAFAC (CP) decomposition is a powerful but computationally challenging tool in modern data analytics. In this paper, we show ways of sampling intermediate steps of alternating minimization algorithms for computing low rank tensor CP decompositions, leading to the sparse alternating least squares (SPALS) method. Specifically, we sample the Khatri-Rao product, which arises as a...

In 2006 Netflix announced a million dollar prize to the first team that could beat their Cinematch recommendation system by 10% on a particular test data set. Specifically, given over 100 million ratings, 1-5, from 480,189 different users and 17,770 different movies, the goal was to produce predictions for the test set that minimize the root mean square error. Cinematch scored an rmse of .9525,...

This document describes the Sequential Alternating Least Squares (SeALS) tool developed in MATLAB. Five examples are included – scalar unstable system, smooth two dimensional system, an inverted pendulum on a moving cart, a VTOL aircraft, and a quadcopter – to illustrate the use of SeALS. The theoretical background of this tool is given by [1]. The tool is available at [2]. The rest of this doc...

Color correction is an image-altering technique that modifies image color in such a way it matches reference image. Many approaches have already been proposed by various researchers; however, those models unable to reduce errors between two images, which results inefficiency and poor-quality images. This research paper presents effective improved model wherein alternate least square (ALS) root ...

Recommendation system can predict the ratings of users to items by leveraging machine learning algorithms. The use recommendation systems is common in e-commerce websites now-a-days. Since enormous amounts data including users’ click streams, purchase history, demographics, social networking comments and user-item are stored databases, volume getting bigger at high speed, sparse. However, recom...