I have a collection of objects with properties that I measure. For each object, I obtain a vector of real numbers describing that object. Each object results in a vector having a different length. I also measure, say, the mass of the object, and I now want to relate the vector of things I've measured to the mass.
It's common in my field to extract features from this vector, e.g. take an average or some linear combinations of the values; and then use those extracted features to infer the mass (or whatever) using for example neural networks. It was recently shown, however, that a very complex combination of the elements of the vector result in a much better model of the mass.
There are still residuals in this model, however, even when working on simulated data. Presumably then there is a better way out there to manipulate these variable-length vectors in order to get a better model.
I am wondering if it is possible to do machine learning with real-valued input vectors of all different lengths. I know for text mining there are things like the bag-of-words approach, but it is unclear how such a method would work on real-valued vectors. Is there any research in this area?