Abstract:
Learning new global relations based on an initial
affinity of the database objects has shown significant
improvements in similarity retrievals. Locally constrained diffusion process is one of the recent effective
tools in learning the intrinsic manifold structure of
a given data. Existing methods, which constrained
the diffusion process locally, have problems - manual
choice of optimal local neighborhood size, do not
allow for intrinsic relation among the neighbors, fix
initialization vector to extract dense neighbor - which
negatively affect the affinity propagation. We propose
a new approach, which alleviate these issues, based
on some properties of a family of quadratic optimization problems related to dominant sets, a well-known
graph-theoretic notion of a cluster which generalizes
the concept of a maximal clique to edge-weighted
graphs. In particular, we show that by properly controlling a regularization parameter which determines
the structure and the scale of the underlying problem,
we are in a position to extract dominant set cluster
which is constrained to contain user-provided query.
Experimental results on standard benchmark datasets
show the effectiveness of the proposed approach.
