Latent Complete Row Space Recovery for Multi-view Subspace Clustering
This work addresses multi-view clustering for applications like image processing and video surveillance, offering a more efficient and scalable approach compared to existing methods.
The paper tackled the problem of multi-view subspace clustering by proposing the Latent Complete Row Space Recovery (LCRSR) method, which avoids time-consuming graph construction and recovers a latent representation to determine subspace membership, resulting in improved scalability and validated effectiveness on various datasets.
Multi-view subspace clustering has been applied to applications such as image processing and video surveillance, and has attracted increasing attention. Most existing methods learn view-specific self-representation matrices, and construct a combined affinity matrix from multiple views. The affinity construction process is time-consuming, and the combined affinity matrix is not guaranteed to reflect the whole true subspace structure. To overcome these issues, the Latent Complete Row Space Recovery (LCRSR) method is proposed. Concretely, LCRSR is based on the assumption that the multi-view observations are generated from an underlying latent representation, which is further assumed to collect the authentic samples drawn exactly from multiple subspaces. LCRSR is able to recover the row space of the latent representation, which not only carries complete information from multiple views but also determines the subspace membership under certain conditions. LCRSR does not involve the graph construction procedure and is solved with an efficient and convergent algorithm, thereby being more scalable to large-scale datasets. The effectiveness and efficiency of LCRSR are validated by clustering various kinds of multi-view data and illustrated in the background subtraction task.