  We propose a Classification Via Clustering (CVC) algorithm which enables
existing clustering methods to be efficiently employed in classification
problems. In CVC, training and test data are co-clustered and class-cluster
distributions are used to find the label of the test data. To determine an
efficient number of clusters, a Semi-supervised Hierarchical Clustering (SHC)
algorithm is proposed. Clusters are obtained by hierarchically applying two-way
NCut by using signs of the Fiedler vector of the normalized graph Laplacian. To
this end, a Direct Fiedler Vector Computation algorithm is proposed. The graph
cut is based on the data structure and does not consider labels. Labels are
used only to define the stopping criterion for graph cut. We propose clustering
to be performed on the Grassmannian manifolds facilitating the formation of
spectral ensembles. The proposed algorithm outperformed state-of-the-art
image-set classification algorithms on five standard datasets.
