Subspace clustering without knowing the number of clusters: A parameter free approach
This addresses a practical limitation in subspace clustering for high-dimensional data analysis, though it appears incremental as it builds on existing clustering frameworks.
The authors tackled the problem of subspace clustering without requiring prior knowledge of the number of clusters or additional parameters, proposing a parameter-free method that merges clusters based on statistical differences in angles between data points. The method showed competitive performance compared to state-of-the-art techniques on synthetic and real datasets.
Subspace clustering, the task of clustering high dimensional data when the data points come from a union of subspaces is one of the fundamental tasks in unsupervised machine learning. Most of the existing algorithms for this task require prior knowledge of the number of clusters along with few additional parameters which need to be set or tuned apriori according to the type of data to be clustered. In this work, a parameter free method for subspace clustering is proposed, where the data points are clustered on the basis of the difference in statistical distribution of the angles subtended by the data points within a subspace and those by points belonging to different subspaces. Given an initial fine clustering, the proposed algorithm merges the clusters until a final clustering is obtained. This, unlike many existing methods, does not require the number of clusters apriori. Also, the proposed algorithm does not involve the use of an unknown parameter or tuning for one. %through cross validation. A parameter free method for producing a fine initial clustering is also discussed, making the whole process of subspace clustering parameter free. The comparison of proposed algorithm's performance with that of the existing state-of-the-art techniques in synthetic and real data sets, shows the significance of the proposed method.