We consider the bridge linear regression modeling, which can produce a sparse or non-sparse model. A crucial point in the model building process is the selection of adjusted parameters including a regularization parameter and a tuning parameter in bridge regression models. The choice of the adjusted parameters can be viewed as a model selection and evaluation problem. We propose a model selection criterion for evaluating bridge regression models in terms of Bayesian approach. This selection criterion enables us to select the adjusted parameters objectively. We investigate the effectiveness of our proposed modeling strategy through some numerical examples.

For example, word recognition can greatly benefit from the availability of joint audiovisual measurements [17]. Person recognition and verification can be performed much more accurately by fusing information from several modalities such as facial images, iris scans, voice recordings, and handwritings. A major difficulty in fusing multiple sources is that one can often access only distinct labeled training sets for the different domains and does not have paired labeled examples from all domains. Suppose, for instance, we wish to perform audiovisual gender recognition. There are numerous existing data-sets of labeled voice recordings as well as labeled data-sets of facial images. However, there are only a few jointly labeled audiovisual data-sets, with a limited number of different subjects each.

We introduce a new family of estimators for unnormalized statistical models. Our family of estimators is parameterized by two nonlinear functions and uses a single sample from an auxiliary distribution, generalizing Maximum Likelihood Monte Carlo estimation of Geyer and Thompson (1992). The family is such that we can estimate the partition function like any other parameter in the model. The estimation is done by optimizing an algebraically simple, well defined objective function, which allows for the use of dedicated optimization methods. We establish consistency of the estimator family and give an expression for the asymptotic covariance matrix, which enables us to further analyze the influence of the nonlinearities and the auxiliary density on estimation performance. Some estimators in our family are particularly stable for a wide range of auxiliary densities. Interestingly, a specific choice of the nonlinearity establishes a connection between density estimation and classification by nonlinear logistic regression. Finally, the optimal amount of auxiliary samples relative to the given amount of the data is considered from the perspective of computational efficiency.

Online community managers work towards building and managing communities around a given brand or topic. A risk imposed on such managers is that their community may die out and its utility diminish to users. Understanding what drives attention to content and the dynamics of discussions in a given community informs the community manager and/or host with the factors that are associated with attention. In this paper we gain insights into the idiosyncrasies that individual community forums exhibit in their attention patterns and how the factors that impact activity differ. We glean such insights by using logistic regression models for identifying seed posts and explore the effectiveness of a range of features. Our findings show that the discussion behaviour of different communities is clearly impacted by different factors.

Web-based social networks typically use public trust systems to facilitate interactions between strangers. These systems can be corrupted by misleading information spread under the cover of anonymity, or exhibit a strong bias towards positive feedback, originating from the fear of reciprocity. Trust propagation algorithms seek to overcome these shortcomings by inferring trust ratings between strangers from trust ratings between acquaintances and the structure of the network that connects them. We investigate a trust propagation algorithm that is based on user triads where the trust one user has in another is predicted based on an intermediary user. The propagation function can be applied iteratively to propagate trust along paths between a source user and a target user. We evaluate this approach using the trust network of the CouchSurfing community, which consists of 7.6M trust-valued edges between 1.1M users. We show that our model out-performs one that relies only on the trustworthiness of the target user (a kind of public trust system). In addition, we show that performance is significantly improved by bringing in user-level variability using mixed-effects regression models.

We propose a logistic regression model taking into account two analytically different sets of factors–structure and action. The factors include individual, dyadic, and triadic properties between ego and alter whose tie breakup is under consideration. From the fitted model using a large-scale data, we discover 5 structural and 7 actional variables to have significant explanatory power for unfollow. One unique finding from our quantitative analysis is that people appreciate receiving acknowledgements from others even in virtually unilateral communication relationships and are less likely to unfollow them: people are more of a receiver than a giver.

Unlike traditional firms, open collaborative systems rely on volunteers to operate, and many communities struggle to maintain enough contributors to ensure the quality and quantity of content. However, Wikipedia has historically faced the exact opposite problem: too much participation, particularly from users who, knowingly or not, do not share the same norms as veteran Wikipedians. During its period of exponential growth, the Wikipedian community developed specialized socio-technical defense mechanisms to protect itself from the negatives of massive participation: spam, vandalism, falsehoods, and other damage. Yet recently, Wikipedia has faced a number of high-profile issues with recruiting and retaining new contributors. In this paper, we first illustrate and describe the various defense mechanisms at work in Wikipedia, which we hypothesize are inhibiting newcomer retention. Next, we present results from an experiment aimed at increasing both the quantity and quality of editors by altering various elements of these defense mechanisms, specifically pre-scripted warnings and notifications that are sent to new editors upon reverting or rejecting contributions. Using logistic regressions to model new user activity, we show which tactics work best for different populations of users based on their motivations when joining Wikipedia. In particular, we found that personalized messages in which Wikipedians identified themselves in active voice and took direct responsibility for rejecting an editor’s contributions were much more successful across a variety of outcome metrics than the current messages, which typically use an institutional and passive voice.

We study the problem of learning high dimensional regression models regularized by a structured-sparsity-inducing penalty that encodes prior structural information on either input or output sides. We consider two widely adopted types of such penalties as our motivating examples: 1) overlapping group lasso penalty, based on the l1/l2 mixed-norm penalty, and 2) graph-guided fusion penalty. For both types of penalties, due to their non-separability, developing an efficient optimization method has remained a challenging problem. In this paper, we propose a general optimization approach, called smoothing proximal gradient method, which can solve the structured sparse regression problems with a smooth convex loss and a wide spectrum of structured-sparsity-inducing penalties. Our approach is based on a general smoothing technique of Nesterov. It achieves a convergence rate faster than the standard first-order method, subgradient method, and is much more scalable than the most widely used interior-point method. Numerical results are reported to demonstrate the efficiency and scalability of the proposed method.

Many modern data mining applications are concerned with the analysis of datasets in which the observations are described by paired high-dimensional vectorial representations or "views". Some typical examples can be found in web mining and genomics applications. In this article we present an algorithm for data clustering with multiple views, Multi-View Predictive Partitioning (MVPP), which relies on a novel criterion of predictive similarity between data points. We assume that, within each cluster, the dependence between multivariate views can be modelled by using a two-block partial least squares (TB-PLS) regression model, which performs dimensionality reduction and is particularly suitable for high-dimensional settings. The proposed MVPP algorithm partitions the data such that the within-cluster predictive ability between views is maximised. The proposed objective function depends on a measure of predictive influence of points under the TB-PLS model which has been derived as an extension of the PRESS statistic commonly used in ordinary least squares regression. Using simulated data, we compare the performance of MVPP to that of competing multi-view clustering methods which rely upon geometric structures of points, but ignore the predictive relationship between the two views. State-of-art results are obtained on benchmark web mining datasets.

We consider the problem of online linear regression on individual sequences. The goal in this paper is for the forecaster to output sequential predictions which are, after T time rounds, almost as good as the ones output by the best linear predictor in a given L1-ball in R^d. We consider both the cases where the dimension d is small and large relative to the time horizon T. We first present regret bounds with optimal dependencies on the sizes U, X and Y of the L1-ball, the input data and the observations. The minimax regret is shown to exhibit a regime transition around the point d = sqrt(T) U X / (2 Y). Furthermore, we present efficient algorithms that are adaptive, i.e., they do not require the knowledge of U, X, and Y, but still achieve nearly optimal regret bounds.