Suppose we have a set of objects $X$ (e.g. individual humans). Suppose also that humans can be described by a set of (potentially very high-dimensional) variables $V_i$, (e.g. $V_1$ is a picture of their face, $V_2$ is their name, $V_3$ is a picture of their house, etc).
Now suppose we have multiple datasets $(D_i)$ all of which capture very different information about $X$, i.e.:
the $D_i$ have very different types. e.g. $D_1$ contains images, while $D_2$ contains audio
the $D_i$ have different levels of aggregation. e.g. perhaps $D_1$ gives pictures of individuals, and their location, while $D_2$ gives wealth, but only contains information on the average wealth at the city level.
The $D_i$ vary in different ways. e.g. all elements of $D_1$ have individuals from New York, but vary in terms of income level, while all elements of $D_2$ are from various different cities but are all upper middle class.
I want to learn more about learning problems like this. What keywords/resources should I look for?
Is there a name for such "very varied datasets about the same underlying objects"? (or a name for the problem containing such datasets?) I'd like to look this up and read more about it. I guess it has to do with "big data" but I'm hoping there are more specific keywords I could search for.
Are there names for algorithms that work for these problems? (or other keywords that can help me look for algorithms)
Is there theory about this type of problem? e.g. in statistical learning theory.