An Experimental Study of Design Information Reuse

Proceedings of the 4th International Conference on Design Theory and Methodology. 1992
An Experimental Study of Design Information Reuse
Vinod Baya, Jody Gevins, Catherine Baudin, Ade Magogunje, Larry Leifer

We are reporting results and experiences from an experimental study conducted to study the nature of design information reuse during redesign. Starting with a detailed study of the questioning behavior of two designers, we have developed a framework for understanding the character and the basic constitution of information that should be recorded during design for the reuse process to be useful and productive.

This study lays the ground work for future work in recording, characterizing and indexing design information as it is generated during the design process.

1 Introduction Design in any engineering domain is a very complex activity. We as researchers look at this activity from various perspectives such as technical, methodological, social 1 Jody Gevins is a contractor at Sterling software systems and Catherine Baudin at RECOM inc.

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.