[arXiv] BigDataFr recommends: The Scalable Langevin Exact Algorithm: Bayesian Inference for Big Data

BigDataFr recommends: The Scalable Langevin Exact Algorithm: Bayesian Inference for Big Data Subjects: Methodology (stat.ME); Computation (stat.CO) […] This paper introduces a class of Monte Carlo algorithms which are based upon simulating a Markov process whose quasi-stationary distribution coincides with the distribution of interest. This differs fundamentally from, say, current Markov chain Monte Carlo in […]

[arXiv] BigDataFr recommends: Human-Algorithm Interaction Biases in the Big Data Cycle: A Markov Chain Iterated Learning Framework

BigDataFr recommends: Human-Algorithm Interaction Biases in the Big Data Cycle: A Markov Chain Iterated Learning Framework Comments: This research was supported by National Science Foundation grant NSF-1549981 Subjects: Learning (cs.LG); Human-Computer Interaction (cs.HC) […] Early supervised machine learning algorithms have relied on reliable expert labels to build predictive models. However, the gates of data generation […]

[arXiv] BigDataFr recommends: Clustering Mixed Datasets Using Homogeneity Analysis with Applications to Big Data

BigDataFr recommends: Clustering Mixed Datasets Using Homogeneity Analysis with Applications to Big Data Subjects: Machine Learning (stat.ML) […] Clustering datasets with a mix of continuous and categorical attributes is encountered routinely by data analysts. This work presents a method to clustering such datasets using Homogeneity Analysis. An Optimal Euclidean representation of mixed datasets is obtained […]

[arXiv] BigDataFr recommends: Asynchronous Parallel Algorithms for Nonconvex Big-Data Optimization

BigDataFr recommends: Asynchronous Parallel Algorithms for Nonconvex Big-Data Optimization: Model and Convergence Subjects: Optimization and Control (math.OC); Distributed, Parallel, and Cluster Computing (cs.DC) […] We propose a novel asynchronous parallel algorithmic framework for the minimization of the sum of a smooth nonconvex function and a convex nonsmooth regularizer, subject to both convex and nonconvex constraints. […]

[Datasciencecentral] BigDataFr recommends: A methodology for solving problems with DataScience for Internet of Things – Full – #iot

BigDataFr recommends: A methodology for solving problems with DataScience for Internet of Things – Part 1 and 2 […] This two part blog is based on my forthcoming book: Data Science for Internet of Things. It is also the basis for the course I teach Data Science for Internet of Things Course. I will be […]

[arXiv] BigDataFr recommends: Representation of functions on big data associated with directed graphs

BigDataFr recommends: Representation of functions on big data associated with directed graphs Subjects: Classical Analysis and ODEs (math.CA) […] This paper is an extension of the previous work of Chui, Filbir, and Mhaskar (Appl. Comput. Harm. Anal. 38 (3) 2015:489-509), not only from numeric data to include non-numeric data as in that paper, but also […]

[arXiv] BigDataFr recommends: Measuring Economic Activities of China with Mobile Big Data

BigDataFr recommends: Measuring Economic Activities of China with Mobile Big Data Subjects: Social and Information Networks (cs.SI); Computers and Society (cs.CY) […] Emerging trends in smartphones, online maps, social media, and the resulting geo-located data, provide opportunities to collect traces of people’s socio-economical activities in a much more granular and direct fashion, triggering a revolution […]