arXiv subjects

Abdelkader Baggag

Publications and source records attributed to Abdelkader Baggag.

3 recordsLinked to original sources

arXiv2024

ClustML: A Measure of Cluster Pattern Complexity in Scatterplots Learnt from Human-labeled Groupings

Visual quality measures (VQMs) are designed to support analysts by automatically detecting and quantifying patterns in visualizations. We propose a new VQM for visual grouping patterns in scatterplots, called ClustML, which is trained on previously collected human subject judgments. Our model encodes scatterplots in the parametric space of a Gaussian Mixture Model and uses a classifier trained on human judgment data to estimate the perceptual complexity of grouping patterns. The numbers of initial mixture components and final combined groups. It improves on existing VQMs, first, by better estimating human judgments on two-Gaussian cluster patterns and, second, by giving higher accuracy when ranking general cluster patterns in scatterplots. We use it to analyze kinship data for genome-wide association studies, in which experts rely on the visual analysis of large sets of scatterplots. We make the benchmark datasets and the new VQM available for practical use and further improvements.

cs.HC↗

arXiv2020

Entropy, Free Energy, and Work of Restricted Boltzmann Machines

A restricted Boltzmann machine is a generative probabilistic graphic network. A probability of finding the network in a certain configuration is given by the Boltzmann distribution. Given training data, its learning is done by optimizing parameters of the energy function of the network. In this paper, we analyze the training process of the restricted Boltzmann machine in the context of statistical physics. As an illustration, for small size Bar-and-Stripe patterns, we calculate thermodynamic quantities such as entropy, free energy, and internal energy as a function of training epoch. We demonstrate the growth of the correlation between the visible and hidden layers via the subadditivity of entropies as the training proceeds. Using the Monte-Carlo simulation of trajectories of the visible and hidden vectors in configuration space, we also calculate the distribution of the work done on the restricted Boltzmann machine by switching the parameters of the energy function. We discuss the Jarzynski equality which connects the path average of the exponential function of the work and the difference in free energies before and after training.

cond-mat.dis-nn↗

arXiv2019

Fast Computation of Katz Index for Efficient Processing of Link Prediction Queries

Network proximity computations are among the most common operations in various data mining applications, including link prediction and collaborative filtering. A common measure of network proximity is Katz index, which has been shown to be among the best-performing path-based link prediction algorithms. With the emergence of very large network databases, such proximity computations become an important part of query processing in these databases. Consequently, significant effort has been devoted to developing algorithms for efficient computation of Katz index between a given pair of nodes or between a query node and every other node in the network. Here, we present LRC-Katz, an algorithm based on indexing and low-rank correction to accelerate Katz index-based network proximity queries. Using a variety of very large real-world networks, we show that LRC-Katz outperforms the fastest existing method, Conjugate Gradient, for a wide range of parameter values. We also show that this acceleration in the computation of Katz index can be used to drastically improve the efficiency of processing link prediction queries in very large networks. Motivated by this observation, we propose a new link prediction algorithm that exploits modularity of networks that are encountered in practical applications. Our experimental results on the link prediction problem show that our modularity based algorithm significantly outperforms the state-of-the-art link prediction Katz method.

cs.SI↗