Distributed Research Teams: Meeting Asynchronously in Virtual Space

Journal of Computer Mediated Communication, JCMC 4 (4), 1999
Distributed Research Teams: Meeting Asynchronously in Virtual Space
Lai Adams, Lori Toomey, Elizabeth Churchill
Abstract
 
As computer networks improve, more social and work interactions are carried out “virtually” by geographically separated group members. In this paper we discuss the design of a tool, PAVE, to support remote work interactions among colleagues in different time zones.
 
PAVE extends a 2D graphical MOO and supports synchronous and asynchronous interactions.
 
PAVE logs and indexes activities in the space. This capture facility enables playback and augmentation of meeting interactions by non-collocated group members. Thus, members can participate asynchronously in meetings they could not attend in real time, not just review them.

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.

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