On Estimating Multi-Attribute Choice Preferences using Private Signals and Matrix Factorization

Revealed preference theory studies the possibility of modeling an agent's revealed preferences and the construction of a consistent utility function. However, modeling agent's choices over preference orderings is not always practical and demands strong assumptions on human rationality and data-acquisition abilities. Therefore, we propose a simple generative choice model where agents are assumed to generate the choice probabilities based on latent factor matrices that capture their choice evaluation across multiple attributes. Since the multi-attribute evaluation is typically hidden within the agent's psyche, we consider a signaling mechanism where agents are provided with choice information through private signals, so that the agent's choices provide more insight about his/her latent evaluation across multiple attributes. We estimate the choice model via a novel multi-stage matrix factorization algorithm that minimizes the average deviation of the factor estimates from choice data. Simulation results are presented to validate the estimation performance of our proposed algorithm.Comment: 6 pages, 2 figures, to be presented at CISS conferenc

Probabilistic non-negative matrix factorization and its robust extensions for topic modeling

Copyright © 2017, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. Traditional topic model with maximum likelihood estimate inevitably suffers from the conditional independence of words given the documents topic distribution. In this paper, we follow the generative procedure of topic model and learn the topic-word distribution and topics distribution via directly approximating the word-document co-occurrence matrix with matrix decomposition technique. These methods include: (1) Approximating the normalized document-word conditional distribution with the documents probability matrix and words probability matrix based on probabilistic non-negative matrix factorization (NMF); (2) Since the standard NMF is well known to be non-robust to noises and outliers, we extended the probabilistic NMF of the topic model to its robust versions using ℓ2, 1 -norm and capped ℓ2, 1 -norm based loss functions, respectively. The proposed framework inherits the explicit probabilistic meaning of factors in topic models and simultaneously makes the conditional independence assumption on words unnecessary. Straightforward and efficient algorithms are exploited to solve the corresponding non-smooth and non-convex problems. Experimental results over several benchmark datasets illustrate the effectiveness and superiority of the proposed methods

Classificação de petróleos

The identification of patterns in the crude oil assay data provides useful information for crude oil properties estimation as well as for the refinery operation and logistics. The a priori information about the characteristics of a determined crude improves the logistic concerning which refineries should process it, together with pricing. This work explores data mining techniques over some characterization properties of crude oil assays, in order to group similar crude oils in an unsupervised way. The results show that the derived models are able to find patterns, clustering crudes according these properties. Afterwards, these are compared to a standard classification which is aware only about the oil crude density.A identificação de padrões em dados de ensaios de óleo bruto fornece informações importantes sobre a estimação das propriedades do petróleo, assim como para a operação e cadeia logística das refinarias. A informação a priori sobre as características de determinada amostra de óleo melhora a logística em relação a maneira que as refinarias devem processá-lo, assim como a sua precificação. Essa tese explora técnicas de mineração de dados usando algumas propriedades relevantes para a caracterização dos ensaios, de maneira a agrupar amostras de óleos brutos similares de maneira não-supervisionada. Os resultados mostram que os modelos obtidos são capazes de encontrar padrões ao agrupar as amostras de acordo com essas propriedades. Estes são então comparados à uma classificação comumente usada na indústria, baseada apenas na mensuração da densidade do petróleo