1 research outputs found
Learning Individual Models for Imputation (Technical Report)
Missing numerical values are prevalent, e.g., owing to unreliable sensor
reading, collection and transmission among heterogeneous sources. Unlike
categorized data imputation over a limited domain, the numerical values suffer
from two issues: (1) sparsity problem, the incomplete tuple may not have
sufficient complete neighbors sharing the same/similar values for imputation,
owing to the (almost) infinite domain; (2) heterogeneity problem, different
tuples may not fit the same (regression) model. In this study, enlightened by
the conditional dependencies that hold conditionally over certain tuples rather
than the whole relation, we propose to learn a regression model individually
for each complete tuple together with its neighbors. Our IIM, Imputation via
Individual Models, thus no longer relies on sharing similar values among the k
complete neighbors for imputation, but utilizes their regression results by the
aforesaid learned individual (not necessary the same) models. Remarkably, we
show that some existing methods are indeed special cases of our IIM, under the
extreme settings of the number l of learning neighbors considered in individual
learning. In this sense, a proper number l of neighbors is essential to learn
the individual models (avoid over-fitting or under-fitting). We propose to
adaptively learn individual models over various number l of neighbors for
different complete tuples. By devising efficient incremental computation, the
time complexity of learning a model reduces from linear to constant.
Experiments on real data demonstrate that our IIM with adaptive learning
achieves higher imputation accuracy than the existing approaches