Learning Linear, Sparse, Factorial Codes

In previous work (Olshausen & Field 1996), an algorithm was described for learning linear sparse codes which, when trained on natural images, produces a set of basis functions that are spatially localized, oriented, and bandpass (i.e., wavelet-like). This note shows how the algorithm may be interpreted within a maximum-likelihood framework. Several useful insights emerge from this connection: it makes explicit the relation to statistical independence (i.e., factorial coding), it shows a formal relationship to the algorithm of Bell and Sejnowski (1995), and it suggests how to adapt parameters that were previously fixed.

Factorial Hidden Markov Models

We present a framework for learning in hidden Markov models with distributed state representations. Within this framework, we derive a learning algorithm based on the Expectation--Maximization (EM) procedure for maximum likelihood estimation. Analogous to the standard Baum-Welch update rules, the M-step of our algorithm is exact and can be solved analytically. However, due to the combinatorial nature of the hidden state representation, the exact E-step is intractable. A simple and tractable mean field approximation is derived. Empirical results on a set of problems suggest that both the mean field approximation and Gibbs sampling are viable alternatives to the computationally expensive exact algorithm.

Optimizing Product Line Designs: Efficient Methods and Comparisons

We compare a broad range of optimal product line design methods. The comparisons take advantage of recent advances that make it possible to identify the optimal solution to problems that are too large for complete enumeration. Several of the methods perform surprisingly well, including Simulated Annealing, Product-Swapping and Genetic Algorithms. The Product-Swapping heuristic is remarkable for its simplicity. The performance of this heuristic suggests that the optimal product line design problem may be far easier to solve in practice than indicated by complexity theory.

A process-based cost estimating tool for ship structural designs

by John M. Barentine.

Composición factorial de la versión española de la Spence Children Anxiety Scale (SCAS).

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An??lisis de la estructura factorial de las puntuaciones de la 'Preschool and Kindergarten Behavior Scale' en poblaci??n espa??ola

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Uso de R y GRASS para la obtención de mapas de disponibilidad de hábitat mediante análisis factorial de nicho

It is possible to obtain habitat suitability maps using several applications like "Biomapper" v. 3.1.5 (http://www2.unil.ch/biomapper) or the "adehabitat" library v. 1.2.1, developed to be used within R program (http//www.R-project.org)

Estructura factorial de la adaptaci??n espa??ola de la Escala de Identificaci??n Grupal de Tarrant

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El an??lisis factorial de datos ipsativos : un estudio de simulaci??n

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Evaluación de la equivalencia factorial y métrica de la Sexual Assertiveness Scale (SAS) por sexo.

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Randomization tests for ABAB designs : Comparing-data-divisionspecific and common distributions. 'Pruebas de aleatorizaci??n para dise??os ABAB : comparaci??n entre distribuciones espec??ficas y comunes'

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Desarrollo del cuestionario de agresividad proactiva-reactiva para profesores : estructura factorial y propiedades psicom??tricas

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Adaptaci??n al castellano y estructura factorial del Denison Organizational Culture Survey

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Procedimientos gr??ficos para evaluar la consistencia intra-individual en el an??lisis factorial de ??tems

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An??lisis factorial confirmatorio del ??ndice de Sensibilidad a la Ansiedad para Ni??os

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