Novel Framework for Spectral Clustering using Topological Node Features(TNF)
This work addresses the challenge of improving spectral clustering accuracy for complex data, but it appears incremental as it builds on existing affinity matrix techniques.
The authors tackled the problem of constructing effective affinity matrices for spectral clustering by proposing a framework that uses topological node features to capture local density and structure, and they reported that it outperforms standard methods on synthetic, UCI, and MNIST datasets.
Spectral clustering has gained importance in recent years due to its ability to cluster complex data as it requires only pairwise similarity among data points with its ease of implementation. The central point in spectral clustering is the process of capturing pair-wise similarity. In the literature, many research techniques have been proposed for effective construction of affinity matrix with suitable pair- wise similarity. In this paper a general framework for capturing pairwise affinity using local features such as density, proximity and structural similarity is been proposed. Topological Node Features are exploited to define the notion of density and local structure. These local features are incorporated into the construction of the affinity matrix. Experimental results, on widely used datasets such as synthetic shape datasets, UCI real datasets and MNIST handwritten datasets show that the proposed framework outperforms standard spectral clustering methods.