Multi-View Learning from Disparate Sources for Poverty Mapping

Abstract

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.