Incomplete Multiview Nonnegative Representation Learning with Multiple Graphs

TL;DR: In this article , the authors propose a novel Incomplete multiview nonnegative representation learning model with graph completion and adaptive neighbors (IMNGA), which performs common graph learning, missing graph completion, and consensus nonnegative representations simultaneously, where the common graph on all views and the incomplete graph of each view are used to reconstruct the completed graph of the corresponding view.

TL;DR: In this paper , a generalized robust multiview discriminant analysis (GRMDA) is proposed to obtain a linear transform for each view and for learning multi-view jointly sparse discriminant common subspace, which can achieve both maximal between-class and minimal within-class variation for data from multiple views in a common space.

TL;DR: This survey reviews incomplete multi-view learning (IML), analyzing its generative and discriminative perspectives, missing scenarios, and learning tasks, highlighting practical applications and future research directions to adapt IML for incomplete data and complex missing scenarios.

TL;DR: Wang et al. as mentioned in this paper proposed an Incremental Incomplete Multi-view Unsupervised Feature Selection method (I2MUFS) on incomplete multi-view streaming data, which embeds the unsupervised feature selection into an extended weighted non-negative matrix factorization model.

TL;DR: It is shown that the second smallest eigenvalue of a matrix derived from the netlist gives a provably good approximation of the optimal ratio cut partition cost.

TL;DR: A spectral clustering framework is proposed that achieves this goal by co-regularizing the clustering hypotheses, and two co- regularization schemes are proposed to accomplish this.

TL;DR: This paper proposes a novel NMFbased multi-view clustering algorithm by searching for a factorization that gives compatible clustering solutions across multiple views and designs a novel and effective normalization strategy inspired by the connection between NMF and PLSA.