Learning sparse graphical models for data restoration and multi-label classification

University of Technology Sydney. Faculty of Engineering and Information Technology.Sparse probabilistic graphical models play an important role in structured prediction when the dependency structure is unknown. By inducing sparsity over edge parameters, a typical sparse graphical model can combine structure learning and parameter estimation under a unified optimization framework. In this thesis, we propose three specific sparse graphical models accompanied by their applications in data restoration and multi-label classification respectively. For the data restoration task, we propose random mixed field (RMF) model to explore mixed-attribute correlations among data. The RMF model is capable of handling mixed-attribute data denoising and imputation simultaneously. Meanwhile, RMF employs a structured mean-field variational approach to decouple continuous-discrete interactions to achieve approximate inference. The effectiveness of this model is evaluated on both synthetic and real-world data. For the multi-label classification task, we propose correlated logistic model (CorrLog) and conditional graphical lasso (CGL), to learn conditional label correlations. (1) The CorrLog model characterizes pairwise label correlations via scalar parameters, thus effects in an explicit (or direct) fashion. More specifically, CorrLog extends conventional logistic regression by jointly modelling label correlations. In addition, elastic-net regularization is employed to induce sparsity over the scalar parameters that define label correlations. CorrLog can be efficiently learned by regularized maximum pseudo likelihood estimation which enjoys a satisfying generalization bound. Besides, message passing algorithm is applied to solve the multi-label prediction problem. (2) The CGL model further leverages features in modelling pairwise label correlations in terms of parametric functions of the input features, which effects in an implicit (or indirect) fashion. In general, CGL provides a unified Bayesian framework for structure and parameter learning conditioned on input features. We formulate the multi-label prediction as CGL inference problem, which is solved by a mean field variational approach. Meanwhile, CGL learning is efficient after applying the maximum a posterior (MAP) methodology and solved by a proximal gradient procedure. The effectiveness of CorrLog and CGL are evaluated on several benchmark multi-label classification datasets

WHO ARE RESOURCE NONUSERS AND WHAT CAN THEY TELL US ABOUT NONUSE VALUES? AN APPLICATION TO COASTAL WETLAND RESTORATION

This paper assesses the potential for incomplete definitions of resource use to influence estimates of nonuser WTP, and whether uses underlying certain use values may escape measurement using standard mechanisms applied to distinguish resource users from nonusers. Empirical results are drawn from a stated preference analysis involving coastal wetland restoration.Resource /Energy Economics and Policy,

Do looks matter? A case study on extensive green roofs using discrete choice experiments

Extensive green roofs are a promising type of urban green that can play an important role in climate proofing and ultimately in the sustainability of our cities. Despite their increasingly widespread application and the growing scientific interest in extensive green roofs, their aesthetics have received limited scientific attention. Furthermore, several functional issues occur, as weedy species can colonize the roof, and extreme roof conditions can lead to gaps in the vegetation. Apart from altering the function of a green roof, we also expect these issues to influence the perception of extensive green roofs, possibly affecting their acceptance and application. We therefore assessed the preferences of a self-selected convenience sample of 155 Flemish respondents for visual aspects using a discrete choice experiment. This approach, combined with current knowledge on the psychological aspects of green roof visuals, allowed us to quantify extensive green roof preferences. Our results indicate that vegetation gaps and weedy species, together with a diverse vegetation have a considerable impact on green roof perception. Gaps were the single most important attribute, indicated by a relative importance of ca. 53%, with cost coming in at a close second at ca. 46%. Overall, this study explores the applicability of a stated preference technique to assess an often overlooked aspect of extensive green roofs. It thereby provides a foundation for further research aimed at generating practical recommendations for green roof construction and maintenance

ISER Working Paper

In communities that largely depend on the extraction of natural resources, attitudes towards conservation and development may seem at odds or particularly rigid. With an unprecedented wealth of natural capital, a growing mining sector, strong oil and gas industry, and a politically conservative population, Alaska serves as a case study to measure such attitudes. This research was motivated by a lack of primary ecosystem service valuation studies in Alaska that could be used to assess the public’s perceived value of ecosystem services in order to guide future land use decisions and incentivize land use decisions that minimize negative externalities. A choice experiment was conducted with 224 households in the Matanuska-Susitna Borough, the fastest growing region in Alaska and one of the fastest growing regions in the U.S. Rapid development with few restrictions has led to changes for local ecosystems particularly important to salmon, negative effects on access related to recreation and tourism, and caused conversion of valuable farmland. Study results show that attitudes and values vary regarding future land use and economic development efforts. On average, policy action to improve conditions for local salmon stocks are most valuable to local residents followed by protecting farm and ranch lands as well as public access to recreation sites. Conversely, residents show negative preferences towards rapid population growth and developing local mining, oil and gas, and timber resources but support developing a professional and technical services sector. The quantified welfare changes related to different development scenarios show that focusing on conserving valuable ecosystem services is in the public’s best interest

Observable Persistent Effects of Habitat Management Efforts in the Ozark Highlands After 10 Years

I investigated the lasting impacts of a management plan designed to improve oak regeneration and benefit wildlife in the Ozark Highlands in Madison, Co., AR. To assess the efficacy of the management plan, I used variables relevant to the success and establishment of oak trees. Controlled burns and selective logging were used to thin the canopy, increase ground level productivity, and increase the abundance of small mammals. I used measurements of overstory and understory densities, light availability, and the density of mice in the genus Peromyscus across time to look at the lasting impacts of management. Different treatment plots were used to investigate the impact of each management action separately (Burn or Cut) and in combination (Burn and Cut) relative to unaltered control plots. Measurements were compared between pre-treatment, post-treatment, and 10-years post-treatment time points. I found that a 10-year lapse in management resulted in a complete return to pre-treatment values in overstory density. I also saw a decline below pre-treatment values in understory density and Peromyscus density. Analysis of light availability at the forest floor revealed a persistent effect of treatment. I conclude that while initial treatment was effective, 10 years between management events is too infrequent to achieve the desired long-term changes within my study system. More frequent management may be more effective in meeting the management goals for this Ozark system

Model-Based Method for Social Network Clustering

We propose a simple mixed membership model for social network clustering in this note. A flexible function is adopted to measure affinities among a set of entities in a social network. The model not only allows each entity in the network to possess more than one membership, but also provides accurate statistical inference about network structure. We estimate the membership parameters by using an MCMC algorithm. We evaluate the performance of the proposed algorithm by applying our model to two empirical social network data, the Zachary club data and the bottlenose dolphin network data. We also conduct some numerical studies for different types of simulated networks for assessing the effectiveness of our algorithm. In the end, some concluding remarks and future work are addressed briefly

A Network Model characterized by a Latent Attribute Structure with Competition

The quest for a model that is able to explain, describe, analyze and simulate real-world complex networks is of uttermost practical as well as theoretical interest. In this paper we introduce and study a network model that is based on a latent attribute structure: each node is characterized by a number of features and the probability of the existence of an edge between two nodes depends on the features they share. Features are chosen according to a process of Indian-Buffet type but with an additional random "fitness" parameter attached to each node, that determines its ability to transmit its own features to other nodes. As a consequence, a node's connectivity does not depend on its age alone, so also "young" nodes are able to compete and succeed in acquiring links. One of the advantages of our model for the latent bipartite "node-attribute" network is that it depends on few parameters with a straightforward interpretation. We provide some theoretical, as well experimental, results regarding the power-law behaviour of the model and the estimation of the parameters. By experimental data, we also show how the proposed model for the attribute structure naturally captures most local and global properties (e.g., degree distributions, connectivity and distance distributions) real networks exhibit. keyword: Complex network, social network, attribute matrix, Indian Buffet processComment: 34 pages, second version (date of the first version: July, 2014). Submitte

Augmenting short Cheap Talk scripts with a repeated Opt-Out Reminder in Choice Experiment surveys

Hypothetical bias remains a major problem when valuing non-market goods with stated preference methods. Originally developed for Contingent Valuation studies, Cheap Talk has been found to effectively reduce hypothetical bias in some applications, though empirical results are ambiguous. We discuss reasons why Cheap Talk may fail to effectively remove hypothetical bias, especially in Choice Experiments. In this light, we suggest augmenting Cheap Talk in Choice Experiments with a so-called Opt-Out Reminder. Prior to each single choice set, the Opt-Out Reminder explicitly instructs respondents to choose the opt-out alternative if they find the experimentally designed alternatives too expensive. In an empirical Choice Experiment survey we find the Opt-Out Reminder to significantly reduce total WTP and to some extent also marginal WTP beyond the capability of the Cheap Talk applied without the Opt-Out Reminder. This suggests that rather than merely adopting the Cheap Talk practice directly from Contingent Valuation, it should be adapted to fit the potentially different decision processes and repeated choices structure of the Choice Experiment format. Our results further suggest that augmenting Cheap Talk with a dynamic Opt-Out Reminder can be an effective and promising improvement in the ongoing effort to remedy the particular types of hypothetical bias that potentially continue to invalidate Choice Experiment surveys.Cheap talk, Opt-Out Reminder, Choice Experiments, hypothetical bias, stream re-establishment, opt-out effect