Learning for Multi-Type Subspace Clustering
This addresses a non-trivial generalization in subspace clustering for applications with multiple subspace types, though it appears incremental as it builds on existing deep learning methods.
The paper tackles the problem of multi-type subspace clustering, which is challenging due to issues like type selection and sampling imbalance, by learning non-linear subspace filters via deep MLPs to create clustering-friendly embeddings, and achieves state-of-the-art results on synthetic and real-world datasets.
Subspace clustering has been extensively studied from the hypothesis-and-test, algebraic, and spectral clustering based perspectives. Most assume that only a single type/class of subspace is present. Generalizations to multiple types are non-trivial, plagued by challenges such as choice of types and numbers of models, sampling imbalance and parameter tuning. In this work, we formulate the multi-type subspace clustering problem as one of learning non-linear subspace filters via deep multi-layer perceptrons (mlps). The response to the learnt subspace filters serve as the feature embedding that is clustering-friendly, i.e., points of the same clusters will be embedded closer together through the network. For inference, we apply K-means to the network output to cluster the data. Experiments are carried out on both synthetic and real world multi-type fitting problems, producing state-of-the-art results.