189 research outputs found
Multi-view Graph Convolutional Networks with Differentiable Node Selection
Multi-view data containing complementary and consensus information can
facilitate representation learning by exploiting the intact integration of
multi-view features. Because most objects in real world often have underlying
connections, organizing multi-view data as heterogeneous graphs is beneficial
to extracting latent information among different objects. Due to the powerful
capability to gather information of neighborhood nodes, in this paper, we apply
Graph Convolutional Network (GCN) to cope with heterogeneous-graph data
originating from multi-view data, which is still under-explored in the field of
GCN. In order to improve the quality of network topology and alleviate the
interference of noises yielded by graph fusion, some methods undertake sorting
operations before the graph convolution procedure. These GCN-based methods
generally sort and select the most confident neighborhood nodes for each
vertex, such as picking the top-k nodes according to pre-defined confidence
values. Nonetheless, this is problematic due to the non-differentiable sorting
operators and inflexible graph embedding learning, which may result in blocked
gradient computations and undesired performance. To cope with these issues, we
propose a joint framework dubbed Multi-view Graph Convolutional Network with
Differentiable Node Selection (MGCN-DNS), which is constituted of an adaptive
graph fusion layer, a graph learning module and a differentiable node selection
schema. MGCN-DNS accepts multi-channel graph-structural data as inputs and aims
to learn more robust graph fusion through a differentiable neural network. The
effectiveness of the proposed method is verified by rigorous comparisons with
considerable state-of-the-art approaches in terms of multi-view semi-supervised
classification tasks
Multiview subspace clustering using low-rank representation
The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Multiview subspace clustering is one of the most widely used methods for exploiting the internal structures of multiview data. Most previous studies have performed the task of learning multiview representations by individually constructing an affinity matrix for each view without simultaneously exploiting the intrinsic characteristics of multiview data. In this paper, we propose a multiview low-rank representation (MLRR) method to comprehensively discover the correlation of multiview data for multiview subspace clustering. MLRR considers symmetric low-rank representations (LRRs) to be an approximately linear spatial transformation under the new base, i.e., the multiview data themselves, to fully exploit the angular information of the principal directions of LRRs, which is adopted to construct an affinity matrix for multiview subspace clustering, under a symmetric condition. MLRR takes full advantage of LRR techniques and a diversity regularization term to exploit the diversity and consistency of multiple views, respectively, and this method simultaneously imposes a symmetry constraint on LRRs. Hence, the angular information of the principal directions of rows is consistent with that of columns in symmetric LRRs. The MLRR model can be efficiently calculated by solving a convex optimization problem. Moreover, we present an intuitive fusion strategy for symmetric LRRs from the perspective of spectral clustering to obtain a compact representation, which can be shared by multiple views and comprehensively represents the intrinsic features of multiview data. Finally, the experimental results based on benchmark datasets demonstrate the effectiveness and robustness of MLRR compared with several state-of-the-art multiview subspace clustering algorithms
GMC: GRAPH-BASED MULTI-VIEW CLUSTERING
Multi-see diagram based bunching plans to give grouping answers for multi-see information. Be that as it may, most existing techniques don't give adequate thought to loads of various perspectives and require an extra bunching step to deliver the last groups. They additionally as a rule advance their destinations dependent on fixed diagram similitude frameworks, all things considered. In this paper, we propose an overall Graph-based Multi-see Clustering (GMC) to handle these issues. GMC takes the information chart grids, everything being equal, and breakers them to produce a bound together diagram network. The bound together diagram network thus improves the information chart framework of each view, and furthermore gives the last bunches straightforwardly. The critical oddity of GMC is its learning technique, which can help the learning of each view chart lattice and the learning of the bound together diagram grid in a shared fortification way. An epic multi-see combination strategy can naturally weight every information diagram grid to infer the bound together chart network. A position imperative without presenting a tuning boundary is additionally forced on the chart Laplacian lattice of the brought together grid, which helps segment the information focuses normally into the necessary number of bunches. A rotating iterative streamlining calculation is introduced to enhance the goal work
Tensor Decompositions for Signal Processing Applications From Two-way to Multiway Component Analysis
The widespread use of multi-sensor technology and the emergence of big
datasets has highlighted the limitations of standard flat-view matrix models
and the necessity to move towards more versatile data analysis tools. We show
that higher-order tensors (i.e., multiway arrays) enable such a fundamental
paradigm shift towards models that are essentially polynomial and whose
uniqueness, unlike the matrix methods, is guaranteed under verymild and natural
conditions. Benefiting fromthe power ofmultilinear algebra as theirmathematical
backbone, data analysis techniques using tensor decompositions are shown to
have great flexibility in the choice of constraints that match data properties,
and to find more general latent components in the data than matrix-based
methods. A comprehensive introduction to tensor decompositions is provided from
a signal processing perspective, starting from the algebraic foundations, via
basic Canonical Polyadic and Tucker models, through to advanced cause-effect
and multi-view data analysis schemes. We show that tensor decompositions enable
natural generalizations of some commonly used signal processing paradigms, such
as canonical correlation and subspace techniques, signal separation, linear
regression, feature extraction and classification. We also cover computational
aspects, and point out how ideas from compressed sensing and scientific
computing may be used for addressing the otherwise unmanageable storage and
manipulation problems associated with big datasets. The concepts are supported
by illustrative real world case studies illuminating the benefits of the tensor
framework, as efficient and promising tools for modern signal processing, data
analysis and machine learning applications; these benefits also extend to
vector/matrix data through tensorization. Keywords: ICA, NMF, CPD, Tucker
decomposition, HOSVD, tensor networks, Tensor Train
Identifying disease sensitive and quantitative trait-relevant biomarkers from multidimensional heterogeneous imaging genetics data via sparse multimodal multitask learning
Motivation: Recent advances in brain imaging and high-throughput genotyping techniques enable new approaches to study the influence of genetic and anatomical variations on brain functions and disorders. Traditional association studies typically perform independent and pairwise analysis among neuroimaging measures, cognitive scores and disease status, and ignore the important underlying interacting relationships between these units
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