In real-world applications, it is often that the collected multi-view data are incomplete, i.e., some views of samples are absent. Existing clustering methods for incomplete multi-view data all focus on obtaining a common representation or graph from the available views but neglect the hidden information of missing views and information imbalance of different views. To solve these problems, a novel method, called adaptive graph completion based incomplete multi-view clustering (AGC_IMC), is proposed in this paper. Specifically, AGC_IMC develops a joint framework for graph completion and consensus representation learning, which mainly contains three components, i.e., within-view preservation, between-view inferring, and consensus representation learning. To reduce the negative influence of information imbalance, AGC_IMC introduces some adaptive weights to balance the importance of different views during the consensus representation learning. Importantly, AGC_IMC has the potential to recover the similarity graphs of all views with the optimal cluster structure, which encourages it to obtain a more discriminative consensus representation. Experimental results on five well-known datasets show that AGC_IMC significantly outperforms the state-of-the-art methods.

Algorithms for the generation of a bedfast/floating lake ice product from Sentinel-1A/B synthetic aperture radar (SAR) data were implemented, cross-compared, and validated for various permafrost regions (Alaska, Canada and Russia). The algorithms consisted of: 1) thresholding; 2) Iteration Region Growing with Semantics (IRGS); and 3) K-means. The thresholding algorithm (92.4%) was found to perform slightly better on average than the IRGS algorithm (90.1%), and to outperform K-means (85.3%). The thresholding algorithm was therefore selected for implementation of a processing chain to generate a novel bedfast/floating lake ice product. Using a time series of Sentinel-1 SAR data, the new map product shows the day of year (DOY) when the ice becomes bedfast or remains afloat for individual lake sections.