  Local learning methods are a popular class of machine learning algorithms.
The basic idea for the entire cadre is to choose some non-local model family,
to train many of them on small sections of neighboring data, and then to
`stitch' the resulting models together in some way. Due to the limits of
constraining a training dataset to a small neighborhood, research on
locally-learned models has largely been restricted to simple model families.
Also, since simple model families have no complex structure by design, this has
limited use of the individual local models to predictive tasks. We hypothesize
that, using a sufficiently complex local model family, various properties of
the individual local models, such as their learned parameters, can be used as
features for further learning. This dissertation improves upon the current
state of research and works toward establishing this hypothesis by
investigating algorithms for localization of more complex model families and by
studying their applications beyond predictions as a feature extraction
mechanism. We summarize this generic technique of using local models as a
feature extraction step with the term ``local model feature transformations.''
In this document, we extend the local modeling paradigm to Gaussian processes,
orthogonal quadric models and word embedding models, and extend the existing
theory for localized linear classifiers. We then demonstrate applications of
local model feature transformations to epileptic event classification from EEG
readings, activity monitoring via chest accelerometry, 3D surface
reconstruction, 3D point cloud segmentation, handwritten digit classification
and event detection from Twitter feeds.
