Robust Unsupervised Flexible Auto-weighted Local-Coordinate Concept Factorization for Image Clustering
This work addresses image clustering problems for researchers in machine learning, but it appears incremental as it builds on existing concept factorization methods with added robustness and adaptive features.
The authors tackled high-dimensional data clustering by proposing RFA-LCF, an unsupervised model that integrates robust flexible concept factorization, sparse local-coordinate coding, and adaptive weighting, resulting in enhanced clustering performance as shown in extensive comparisons.
We investigate the high-dimensional data clustering problem by proposing a novel and unsupervised representation learning model called Robust Flexible Auto-weighted Local-coordinate Concept Factorization (RFA-LCF). RFA-LCF integrates the robust flexible CF, robust sparse local-coordinate coding and the adaptive reconstruction weighting learning into a unified model. The adaptive weighting is driven by including the joint manifold preserving constraints on the recovered clean data, basis concepts and new representation. Specifically, our RFA-LCF uses a L2,1-norm based flexible residue to encode the mismatch between clean data and its reconstruction, and also applies the robust adaptive sparse local-coordinate coding to represent the data using a few nearby basis concepts, which can make the factorization more accurate and robust to noise. The robust flexible factorization is also performed in the recovered clean data space for enhancing representations. RFA-LCF also considers preserving the local manifold structures of clean data space, basis concept space and the new coordinate space jointly in an adaptive manner way. Extensive comparisons show that RFA-LCF can deliver enhanced clustering results.