  Among many current data processing systems, the objectives are often not the
reproduction of data, but to compute some answers based on the data resulting
from queries. The similarity identification task is to identify the items in a
database that are similar to a given query item for a given metric. The problem
of compression for similarity identification has been studied in
arXiv:1307.6609 [cs.IT]. Unlike classical compression problems, the focus is
not on reconstructing the original data. Instead, the compression rate is
determined by the desired reliability of the answers. Specifically, the
information measure identification rate characterizes the minimum rate that can
be achieved among all schemes which guarantee reliable answers with respect to
a given similarity threshold. In this paper, we propose a component-based model
for computing correlated similarity queries. The correlated signals are first
decorrelated by the KLT transform. Then, the decorrelated signal is processed
by a distinct D-admissible system for each component. We show that the
component-based model equipped with KLT can perfectly represent the
multivariate Gaussian similarity queries when optimal rate-similarity
allocation applies. Hence, we can derive the identification rate of the
multivariate Gaussian signals based on the component-based model. We then
extend the result to general Gaussian sources with memory. We also study the
models equipped with practical compone\nt systems. We use TC-$\triangle$
schemes that use type covering signatures and triangle-inequality decision
rules as our component systems. We propose an iterative method to numerically
approximate the minimum achievable rate of the TC-$\triangle$ scheme. We show
that our component-based model equipped with TC-$\triangle$ schemes can achieve
better performance than the TC-$\triangle$ scheme unaided on handling the
multivariate Gaussian sources.
