A Concise Yet Effective Model for Non-Aligned Incomplete Multi-View and Missing Multi-Label Learning

Abstract

In real-world applications, learning from data with multi-view and multi-label inevitably confronts with three challenges: missing labels, incomplete views, and non-aligned views. Existing methods mainly concern the first two and commonly need multiple assumptions in attacking them, making even state-ofthe-arts also involve at least two explicit hyper-parameters in their objectives such that model selection is quite difficult. More toughly, these will encounter a failure in dealing with the third challenge, let alone address the three challenges jointly. In this paper, our goal is to meet all of them by presenting a concise yet effective model with only one hyper-parameter in modeling under the least assumption. To make our model more discriminative, we exploit not only the consensus of multiple views but also the global and local structures among multiple labels. More specifically, we introduce an indicator matrix to tackle the first two challenges in a regression manner while align the same individual label and all labels of different views in a common label space to battle the third challenge. During our alignment, we characterize specially the global and the local structures of multiple labels with high-rank and low-rank, respectively. Consequently, the regularization terms involved in modeling are integrated by a single hyper-parameter. Even without viewalignment, it is still confirmed that our method achieves better performance on five real datasets compared to state-of-the-arts.

Human-Guided Functional Connectivity Network Estimation for Chronic Tinnitus Identification: A Modularity View

“…Consequently, the total time complexity of MR is O(γ(N 3 +T N 2 )), where γ is the iteration number. It should be noted that it is not difficult to speed up the algorithm by using ADMM [34], but this is beyond the scope of this work.…”

Non-Aligned Multi-View Multi-Label Classification via Learning View-Specific Labels

“…Multilabel classification results are obtained by a weighted combination of decisions from multiple sources. The classification fusion methods generally consider that although the various views are not explicitly aligned, they can still be implicitly connected through public or shared labels [15]. Nevertheless, intuitively, each view has only a subset of the corresponding labels, meaning each view can only catch a subset of common or shared label data.…”

“…3) The non-aligned multi-view learning problem [15]. In most multi-view learning methods, it is often explicitly or implicitly assumed that the view samples are uniformly aligned, but in reality, it is often difficult to obtain fully consistent multi-view information.…”

“…Most of the existing methods focus on the first two challenges. For the third problem, the literature [15] gives a mitigation scheme: although the samples among views are not aligned, they can still be implicitly connected through common or shared labels to be learned complementarily. However, this strategy is suboptimal because it assumes that all views have a uniform set of labels.…”

Beyond Shared Subspace: A View-Specific Fusion for Multi-View Multi-Label Learning

“…(Zhang et al 2018) leverages matrix factorization to learn a shared subspace representation, and it simultaneously employs Hilbert-Schmidt independence criterion to further remain the consensuses on the shared representation. Besides the above methods, some recent methods have been proposed to learn from multi-view data with weak labels, such as (Tan et al 2018;Wu et al 2019;Li and Chen 2021).…”