  Fine-Grained Change Detection and Regression Analysis are essential in many
applications of ArtificialIntelligence. In practice, this task is often
challenging owing to the lack of reliable ground truth information
andcomplexity arising from interactions between the many underlying factors
affecting a system. Therefore,developing a framework which can represent the
relatedness and reliability of multiple sources of informationbecomes critical.
In this paper, we investigate how techniques in multi-task metric learning can
be applied for theregression of fine-grained change in real data.The key idea
is that if we incorporate the incremental change in a metric of interest
between specific instancesof an individual object as one of the tasks in a
multi-task metric learning framework, then interpreting thatdimension will
allow the user to be alerted to fine-grained change invariant to what the
overall metric isgeneralised to be. The techniques investigated are
specifically tailored for handling heterogeneous data sources,i.e. the input
data for each of the tasks might contain missing values, the scale and
resolution of the values is notconsistent across tasks and the data contains
non-independent and identically distributed (non-IID) instances. Wepresent the
results of our initial experimental implementations of this idea and discuss
related research in thisdomain which may offer direction for further research.
