Group Membership Prediction

The group membership prediction (GMP) problem involves predicting whether or not a collection of instances share a certain semantic property. For instance, in kinship verification given a collection of images, the goal is to predict whether or not they share a {\it familial} relationship. In this context we propose a novel probability model and introduce latent {\em view-specific} and {\em view-shared} random variables to jointly account for the view-specific appearance and cross-view similarities among data instances. Our model posits that data from each view is independent conditioned on the shared variables. This postulate leads to a parametric probability model that decomposes group membership likelihood into a tensor product of data-independent parameters and data-dependent factors. We propose learning the data-independent parameters in a discriminative way with bilinear classifiers, and test our prediction algorithm on challenging visual recognition tasks such as multi-camera person re-identification and kinship verification. On most benchmark datasets, our method can significantly outperform the current state-of-the-art.Comment: accepted for ICCV 201

Batch Multivalid Conformal Prediction

We develop fast distribution-free conformal prediction algorithms for obtaining multivalid coverage on exchangeable data in the batch setting. Multivalid coverage guarantees are stronger than marginal coverage guarantees in two ways: (1) They hold even conditional on group membership -- that is, the target coverage level $1-\alpha$ holds conditionally on membership in each of an arbitrary (potentially intersecting) group in a finite collection $\mathcal{G}$ of regions in the feature space. (2) They hold even conditional on the value of the threshold used to produce the prediction set on a given example. In fact multivalid coverage guarantees hold even when conditioning on group membership and threshold value simultaneously. We give two algorithms: both take as input an arbitrary non-conformity score and an arbitrary collection of possibly intersecting groups $\mathcal{G}$, and then can equip arbitrary black-box predictors with prediction sets. Our first algorithm (BatchGCP) is a direct extension of quantile regression, needs to solve only a single convex minimization problem, and produces an estimator which has group-conditional guarantees for each group in $\mathcal{G}$. Our second algorithm (BatchMVP) is iterative, and gives the full guarantees of multivalid conformal prediction: prediction sets that are valid conditionally both on group membership and non-conformity threshold. We evaluate the performance of both of our algorithms in an extensive set of experiments. Code to replicate all of our experiments can be found at https://github.com/ProgBelarus/BatchMultivalidConformalComment: Code to replicate all of our experiments can be found at https://github.com/ProgBelarus/BatchMultivalidConforma

Multivariate pattern classification of pediatric Tourette syndrome using functional connectivity MRI

Tourette syndrome (TS) is a developmental neuropsychiatric disorder characterized by motor and vocal tics. Individuals with TS would benefit greatly from advances in prediction of symptom timecourse and treatment effectiveness. As a first step, we applied a multivariate method - support vector machine (SVM) classification - to test whether patterns in brain network activity, measured with resting state functional connectivity (RSFC) MRI, could predict diagnostic group membership for individuals. RSFC data from 42 children with TS (8-15 yrs) and 42 unaffected controls (age, IQ, in-scanner movement matched) were included. While univariate tests identified no significant group differences, SVM classified group membership with ~70% accuracy (p < .001). We also report a novel adaptation of SVM binary classification that, in addition to an overall accuracy rate for the SVM, provides a confidence measure for the accurate classification of each individual. Our results support the contention that multivariate methods can better capture the complexity of some brain disorders, and hold promise for predicting prognosis and treatment outcome for individuals with TS

Racism and racial categorization.

Social identity theory predicts that perceivers strongly identified with an in-group will maximize the distinction and maintain a clear boundary between their own and other groups by categorizing others' membership accurately. Two experiments tested the prediction that racially prejudiced individuals, who presumably identify highly with their racial in-group, are more motivated to make accurate racial categorizations than nonprejudiced individuals. Results indicated that prejudiced participants not only took longer to categorize race-ambiguous targets (Experiments 1 and 2), but also made more nonverbal vocalizations when presented with them (Experiment 1), suggesting response hesitation. The results support the hypothesis that, compared to nonprejudiced individuals, prejudiced individuals concern themselves with accurate identification of in-group and out-group members and use caution when making racial categorizations

Social transmission of leadership preference:knowledge of group membership and partisan media reporting moderates perceptions of leadership ability from facial cues to competence and dominance

While first impressions of dominance and competence can influence leadership preference, social transmission of leadership preference has received little attention. The capacity to transmit, store and compute information has increased greatly over recent history, and the new media environment may encourage partisanship (i.e. ‘echo chambers’), misinformation and rumour spreading to support political and social causes and be conducive both to emotive writing and emotional contagion, which may shape voting behaviour. In our pre-registered experiment, we examined whether implicit associations between facial cues to dominance and competence (intelligence) and leadership ability are strengthened by partisan media and knowledge that leaders support or oppose us on a socio-political issue of personal importance. Social information, in general, reduced well-established implicit associations between facial cues and leadership ability. However, as predicted, social knowledge of group membership reduced preferences for facial cues to high dominance and intelligence in out-group leaders. In the opposite-direction to our original prediction, this ‘in-group bias’ was greater under less partisan versus partisan media, with partisan writing eliciting greater state anxiety across the sample. Partisanship also altered the salience of women’s facial appearance (i.e., cues to high dominance and intelligence) in out-group versus in-group leaders. Independent of the media environment, men and women displayed an in-group bias toward facial cues of dominance in same-sex leaders. Our findings reveal effects of minimal social information (facial appearance, group membership, media reporting) on leadership judgements, which may have implications for patterns of voting or socio-political behaviour at the local or national level

Performance Evaluation of Logistic Regression, Linear Discriminant Analysis, and Classification and Regression Trees Under Controlled Conditions

Logistic Regression (LR), Linear Discriminant Analysis (LDA), and Classification and Regression Trees (CART) are common classification techniques for prediction of group membership. Since these methods are applied for similar purposes with different procedures, it is important to evaluate the performance of these methods under different controlled conditions. With this information in hand, researchers can apply the optimal method for certain conditions. Following previous research which reported the effects of conditions such as sample size, homogeneity of variancecovariance matrices, effect size, and predictor distributions, this research focused on effects of correlation between predictor variables, number of the predictor variables, number of the groups in the outcome variable, and group size ratios for the performance of LDA, LR, and CART. Data were simulated with Monte Carlo procedures in R statistical software and a factorial ANOVA with follow-ups was employed to evaluate the effect of conditions on the performance of each technique as measured by proportions of correctly predicted observations for all groups and for the smallest group. In most of the conditions for the two outcome measures, higher performances of CART than LDA and LR were observed. But, in some conditions where there were a higher number of predictor variables and number of groups with low predictor variable correlation, superiority of LR to CART was observed. Meaningful effects of methods of correlation, number or predictor variables, group numbers and group size ratio were observed on prediction accuracy of group membership. Effects of correlation, group size ratio, group number, and number of predictor variables on prediction accuracies were higher for LDA and LR than CART. For the three methods, lower correlation and greater number of predictor variables yielded higher prediction accuracies. Having balanced data rather than imbalanced data and greater group numbers led to lower group membership prediction accuracies for all groups, but having more groups led to better predictions for the small group. In general, based on these results, researchers are encouraged to apply CART in most conditions except for the cases when there are many predictor variables (around 10 or more) and non-binary groups with low correlations between predictor variables, when LR might provide more accurate results

Mixture regression for observational data, with application to functional regression models

In a regression analysis, suppose we suspect that there are several heterogeneous groups in the population that a sample represents. Mixture regression models have been applied to address such problems. By modeling the conditional distribution of the response given the covariate as a mixture, the sample can be clustered into groups and the individual regression models for the groups can be estimated simultaneously. This approach treats the covariate as deterministic so that the covariate carries no information as to which group the subject is likely to belong to. Although this assumption may be reasonable in experiments where the covariate is completely determined by the experimenter, in observational data the covariate may behave differently across the groups. Thus the model should also incorporate the heterogeneity of the covariate, which allows us to estimate the membership of the subject from the covariate. In this paper, we consider a mixture regression model where the joint distribution of the response and the covariate is modeled as a mixture. Given a new observation of the covariate, this approach allows us to compute the posterior probabilities that the subject belongs to each group. Using these posterior probabilities, the prediction of the response can adaptively use the covariate. We introduce an inference procedure for this approach and show its properties concerning estimation and prediction. The model is explored for the functional covariate as well as the multivariate covariate. We present a real-data example where our approach outperforms the traditional approach, using the well-analyzed Berkeley growth study data

Smartphone dependence classification using tensor factorization

Excessive smartphone use causes personal and social problems. To address this issue, we sought to derive usage patterns that were directly correlated with smartphone dependence based on usage data. This study attempted to classify smartphone dependence using a data-driven prediction algorithm. We developed a mobile application to collect smartphone usage data. A total of 41,683 logs of 48 smartphone users were collected from March 8, 2015, to January 8, 2016. The participants were classified into the control group (SUC) or the addiction group (SUD) using the Korean Smartphone Addiction Proneness Scale for Adults (S-Scale) and a face-to-face offline interview by a psychiatrist and a clinical psychologist (SUC = 23 and SUD = 25). We derived usage patterns using tensor factorization and found the following six optimal usage patterns: 1) social networking services (SNS) during daytime, 2) web surfing, 3) SNS at night, 4) mobile shopping, 5) entertainment, and 6) gaming at night. The membership vectors of the six patterns obtained a significantly better prediction performance than the raw data. For all patterns, the usage times of the SUD were much longer than those of the SUC. From our findings, we concluded that usage patterns and membership vectors were effective tools to assess and predict smartphone dependence and could provide an intervention guideline to predict and treat smartphone dependence based on usage data.112Ysciescopu

Group belongingness and procedural justice: Social inclusion and exclusion by peers affects the psychology of voice

The authors focus on the relation between group membership and procedural justice. They argue that whether people are socially included or excluded by their peers influences their reactions to unrelated experiences of procedural justice. Findings from 2 experiments corroborate the prediction that reactions to voice as opposed to no-voice procedures are affected more strongly when people are included in a group than when they are excluded from a group. These findings are extended with a 3rd experiment that shows that people who generally experience higher levels of inclusion in their lives respond more strongly to voice as opposed to no-voice procedures. It is concluded that people's reactions to procedural justice are moderated by people's level of inclusion in social groups