Many data analytics problems involve data coming from multiple sources, sensors, modalities or feature spaces, that describe the object of interest in a unique way, and typically exhibit heterogeneous properties. The varied data sources are termed as views, and the task of learning from such multi-view data is known as multi-view learning. In my thesis, I target the problem of poverty prediction and mapping from multi-source data. Currently, poverty is estimated through intensive household surveys, which is costly and time consuming. The need is to timely and accurately predict poverty and map it to spatially fine-grained baseline data. The primary aim of my thesis is to develop novel multi-view algorithms that combine disparate data sources for poverty mapping. Another aim of my work is to relax the core assumptions faced by existing multi-view learning algorithms, and produce factorized subspaces.

We propose a novel cognitive biometrics modality based on written language-usage of an individual. This is a feasibility study using the Internet-scale blogs, with tens of thousands of authors to create a cognitive fingerprint for an individual. Existing cognitive biometric modalities involve learning from obtrusive sensors placed on human body. Our modality is based on the characteristic pattern of how individuals express their thoughts through written language. The problems of cognitive authentication (1:1 comparison of genuine versus impostor) and identification (1:n search) are formulated. We detail the algorithms to learn a classifier to distinguish between genuine and impostor classes (for authentication) and multiple classes (for identification). We conclude that a cognitive fingerprint can be successfully learnt, using stylistic (writing style), semantic (themes), and syntactic (grammatical) features extracted from blogs. Our methodology shows promising results (with 79% as the area under the ROC (AUC) in case of authentication). For identification, the individual class accuracies are up to 90%. We performed stricter tests to see how our system performs for unseen user, and report the accuracies of 72% (genuine) and 71% (impostor). Such a study lays the groundwork for building alternative cognitive systems. The modality, presented here, is easy to obtain, unobtrusive and needs no additional hardware.

This paper investigates whether the cognitive state of a person can be learnt and used as a novel biometric trait. We explore the idea of using language written by an author, as his/her cognitive fingerprint. The dataset consists of millions of blogs written by thousands of authors on the Internet. Our proposed method learns a classifier that can distinguish between genuine and impostor authors. Our results are encouraging (we report 72% Area under the ROC curve) and show that users do have a distinctive linguistic style, which is evident even when analyzing a corpora as large and diverse as the Internet. When we tested on new authors that the system had never encountered before, our methodology correctly identified genuine authors with 78% accuracy and impostors with 76% accuracy.