Inductive Sparse Subspace Clustering
This addresses a scalability issue for researchers and practitioners using SSC in fast online clustering applications, but it is incremental as it extends an existing method.
The paper tackles the problem of Sparse Subspace Clustering (SSC) being inefficient for out-of-sample data by proposing an inductive method called iSSC, which enables clustering of new data without recomputing the entire graph, with experimental results showing it is promising for this task.
Sparse Subspace Clustering (SSC) has achieved state-of-the-art clustering quality by performing spectral clustering over a $\ell^{1}$-norm based similarity graph. However, SSC is a transductive method which does not handle with the data not used to construct the graph (out-of-sample data). For each new datum, SSC requires solving $n$ optimization problems in O(n) variables for performing the algorithm over the whole data set, where $n$ is the number of data points. Therefore, it is inefficient to apply SSC in fast online clustering and scalable graphing. In this letter, we propose an inductive spectral clustering algorithm, called inductive Sparse Subspace Clustering (iSSC), which makes SSC feasible to cluster out-of-sample data. iSSC adopts the assumption that high-dimensional data actually lie on the low-dimensional manifold such that out-of-sample data could be grouped in the embedding space learned from in-sample data. Experimental results show that iSSC is promising in clustering out-of-sample data.