MobiVis: A Visualization System for Exploring Mobile Data

In Proceedings of IEEE Pacific Visualization Symposium, IEEE VGTC, March, 2008, pp.175-182
MobiVis: A Visualization System for Exploring Mobile Data
Zeqian Shen, Kwan-Liu Ma, Zeqian Shen, Kwan-Liu Ma

The widespread use of mobile devices brings opportunities to capture large-scale, continuous information about human behavior. Mobile data has tremendous value, leading to business opportunities, market strategies, security concerns, etc.

Visual analytics systems that support interactive exploration and discovery are needed to extracting insight from the data. However, visual analysis of complex social-spatial-temporal mobile data presents several challenges.

We have created MobiVis, a visual analytics tool, which incorporates the idea of presenting social and spatial information in one heterogeneous network. The system supports temporal and semantic filtering through an interactive time chart and ontology graph, respectively, such that data subsets of interest can be isolated for close-up investigation.

"Behavior rings," a compact radial representation of individual and group behaviors, is introduced to allow easy comparison of behavior patterns. We demonstrate the capability of MobiVis with the results obtained from analyzing the MIT Reality Mining dataset.

Another publication from the same category: Machine Learning and Data Science

IEEE Computing Conference 2018, London, UK

Regularization of the Kernel Matrix via Covariance Matrix Shrinkage Estimation

The kernel trick concept, formulated as an inner product in a feature space, facilitates powerful extensions to many well-known algorithms. While the kernel matrix involves inner products in the feature space, the sample covariance matrix of the data requires outer products. Therefore, their spectral properties are tightly connected. This allows us to examine the kernel matrix through the sample covariance matrix in the feature space and vice versa. The use of kernels often involves a large number of features, compared to the number of observations. In this scenario, the sample covariance matrix is not well-conditioned nor is it necessarily invertible, mandating a solution to the problem of estimating high-dimensional covariance matrices under small sample size conditions. We tackle this problem through the use of a shrinkage estimator that offers a compromise between the sample covariance matrix and a well-conditioned matrix (also known as the "target") with the aim of minimizing the mean-squared error (MSE). We propose a distribution-free kernel matrix regularization approach that is tuned directly from the kernel matrix, avoiding the need to address the feature space explicitly. Numerical simulations demonstrate that the proposed regularization is effective in classification tasks.