Communications inspired linear discriminant analysis

We study the problem of supervised linear dimensionality reduction, taking an information-theoretic viewpoint. The linear projection matrix is designed by maximizing the mutual information between the projected signal and the class label. By harnessing a recent theoretical result on the gradient of mutual information, the above optimization problem can be solved directly using gradient descent, without requiring simplification of the objective function. Theoretical analysis and empirical comparison are made between the proposed method and two closely related methods, and comparisons are also made with a method in which Rényi entropy is used to define the mutual information (in this case the gradient may be computed simply, under a special parameter setting). Relative to these alternative approaches, the proposed method achieves promising results on real datasets. Copyright 2012 by the author(s)/owner(s).

Man's search for meaning: The case of Legos

We investigate how perceived meaning influences labor supply. In a laboratory setting, we manipulate the perceived meaning of simple, repetitive tasks and find a strong influence on subjects' labor supply. Despite the fact that the wage and the task are identical across the conditions in each experiment, subjects in the less meaningful conditions exhibit reservation wages that are consistently much higher than the subjects in the more meaningful conditions. The result replicates across different types of tasks. Moreover, in the more meaningful conditions, subjects' productivity influences labor supply more strongly. © 2008 Elsevier B.V. All rights reserved.