  An application of the POCS-based clustering algorithm (POCS stands for
Projection Onto Convex Set), a novel clustering technique, for feature
embedding clustering problems is proposed in this paper. The POCS-based
clustering algorithm applies the POCS's convergence property to clustering
problems and has shown competitive performance when compared with that of other
classical clustering schemes in terms of clustering error and execution speed.
Specifically, the POCS-based clustering algorithm treats each data point as a
convex set and applies a parallel projection operation from every cluster
prototype to corresponding data members in order to minimize the objective
function and update the prototypes. The experimental results on the synthetic
embedding datasets extracted from the 5 Celebrity Faces and MNIST datasets show
that the POCS-based clustering algorithm can perform with favorable results
when compared with those of other classical clustering schemes such as the
K-Means and Fuzzy C-Means algorithms in feature embedding clustering problems.
