Constructing Robust Affinity Graphs for Spectral Clustering

• Published in: 2014 IEEE Conference on Computer Vision and Pattern Recognition pp. 1450 - 1457
• Main Authors: Zhu, Xiatian, Loy, Chen Change, Gong, Shaogang
• Format: Conference Proceeding Journal Article
• Language: English
• Published: IEEE 01.06.2014
• Subjects: Affinity; Clustering; Computer vision; Construction; Data models; Data structures; discriminative feature subspaces; Graphs; Noise measurement; Pattern recognition; random forests; Robust affinity graphs; Robustness; Similarity; Spectra; spectral clustering; subtle similarity; Training; Vegetation; Videos; Visualization; weak proximity
• ISSN: 1063-6919; 1063-6919; 2575-7075
• DOI: 10.1109/CVPR.2014.188

Spectral clustering requires robust and meaningful affinity graphs as input in order to form clusters with desired structures that can well support human intuition. To construct such affinity graphs is non-trivial due to the ambiguity and uncertainty inherent in the raw data. In contrast to most existing clustering methods that typically employ all available features to construct affinity matrices with the Euclidean distance, which is often not an accurate representation of the underlying data structures, we propose a novel unsupervised approach to generating more robust affinity graphs via identifying and exploiting discriminative features for improving spectral clustering. Specifically, our model is capable of capturing and combining subtle similarity information distributed over discriminative feature subspaces for more accurately revealing the latent data distribution and thereby leading to improved data clustering, especially with heterogeneous data sources. We demonstrate the efficacy of the proposed approach on challenging image and video datasets.