Score Function Features for Discriminative Learning: Matrix and Tensor Framework

Feature learning forms the cornerstone for tackling challenging learning problems in domains such as speech, computer vision and natural language processing. In this paper, we consider a novel class of matrix and tensor-valued features, which can be pre-trained using unlabeled samples. We present efficient algorithms for extracting discriminative information, given these pre-trained features and labeled samples for any related task. Our class of features are based on higher-order score functions, which capture local variations in the probability density function of the input. We establish a theoretical framework to characterize the nature of discriminative information that can be extracted from score-function features, when used in conjunction with labeled samples. We employ efficient spectral decomposition algorithms (on matrices and tensors) for extracting discriminative components. The advantage of employing tensor-valued features is that we can extract richer discriminative information in the form of an overcomplete representations. Thus, we present a novel framework for employing generative models of the input for discriminative learning.Comment: 29 page

The Skills Shortage and the Payoff to Vocational Education

[Excerpt] Skill demands also appear to be rising within occupations. Increasing numbers of manufacturing workers are working in production cells in which every member of the team is expected to learn every job. Production workers are being given responsibilities--quality checking, statistical process control (SPC) record keeping, resetting machines shown by SPC to be straying from target dimensions, redesigning the layout of the machines in the production cell--that used to be the sole province of supervisors, specialized technicians and industrial engineers. What implications do these changes in skill demands have for the payoff to high school vocational education? Are workers who develop the technical skills taught in trade and technical programs, in fact, more productive when they get a job in the field? Are the skills taught in these programs still valued by the labor market? Has the payoff to high school vocational training increased along with the payoff to other skills? What changes in the way vocational education is delivered are implied by the tight labor markets for highly skilled workers? This paper attempts to answer these questions by examining four different kinds of evidence on the economic payoffs to occupationally specific training in high school