Flexible Multi-View Dimensionality Co-Reduction

Zhang, Changqing; Fu, Huazhu; Hu, Qinghua; Zhu, Pengfei; Cao, Xiaochun

doi:10.1109/tip.2016.2627806

Cited by 126 publications

(32 citation statements)

References 38 publications

(44 reference statements)

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“…The number of RMS features is much larger than that of subjects, especially for dFC; and more importantly, many features may be irrelevant to the classification task. Directly training a machine‐learning model on high‐dimensional small sample data tends to yield poor generalization performance because of the overfitting phenomenon [Chen et al, , Zhang et al, ]. In addition, it also makes interpretation of the results quite difficult.…”

Section: Methodsmentioning

confidence: 99%

Extraction of dynamic functional connectivity from brain grey matter and white matter for MCI classification

Chen

Zhang

et al. 2017

Human Brain Mapping

168

123

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Brain functional connectivity (FC) extracted from resting-state fMRI (RS-fMRI) has become a popular approach for diagnosing various neurodegenerative diseases, including Alzheimer's disease (AD) and its prodromal stage, mild cognitive impairment (MCI). Current studies mainly construct the FC networks between grey matter (GM) regions of the brain based on temporal co-variations of the blood oxygenation level-dependent (BOLD) signals, which reflects the synchronized neural activities. However, it was rarely investigated whether the FC detected within the white matter (WM) could provide useful information for diagnosis. Motivated by the recently proposed functional correlation tensors (FCT) computed from RS-fMRI and used to characterize the structured pattern of local FC in the WM, we propose in this paper a novel MCI classification method based on the information conveyed by both the FC between the GM regions and that within the WM regions. Specifically, in the WM, the tensor-based metrics (e.g., fractional anisotropy [FA], similar to the metric calculated based on diffusion tensor imaging [DTI]) are first calculated based on the FCT and then summarized along each of the major WM fiber tracts connecting each pair of the brain GM regions. This could capture the functional information in the WM, in a similar network structure as the FC network constructed for the GM, based only on the same RS-fMRI data. Moreover, a sliding window approach is further used to partition the voxel-wise BOLD signal into multiple short overlapping segments. Then, both the FC and FCT between each pair of the brain regions can be calculated based on the BOLD signal segments in the GM and WM, respectively. In such a way, our method can generate dynamic FC and dynamic FCT to better capture functional information in both GM and WM and further integrate them together by using our developed feature extraction, selection, and ensemble learning algorithms. The experimental results verify that the dynamic FCT can provide valuable functional information in the WM; by combining it with the dynamic FC in the GM, the diagnosis accuracy for MCI subjects can be significantly improved even using RS-fMRI data alone.

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Section: Methodsmentioning

confidence: 99%

Extraction of dynamic functional connectivity from brain grey matter and white matter for MCI classification

Chen

Zhang

et al. 2017

Human Brain Mapping

168

123

View full text Add to dashboard Cite

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“…To explore nonlinear correlations, DCCA [13] extends CCA using deep neural networks, deep CCA [6] further extends CCA with deep neural networks. Different from CCA, Multi-view Dimensionality co-reduction (MDcR) [14] applies the kernel matching to regularize the dependence across multiple views. Inspired by deep learning, semi-nonnegative matrix factorization is utilized to find a common representation including consistent information of multiple views [15].…”

Section: Related Workmentioning

confidence: 99%

Cross-View Equivariant Auto-Encoder

Wan

Zhang

Geng

et al. 2021

2021 IEEE International Conference on Multimedia and Expo (ICME)

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Unsupervised representation learning on multi-view data (multiple types of features or modalities) becomes a compelling topic in machine learning. Most existing methods focus on directly projecting different views into a common space to explore the consistency across different views. Although simple, the underlying relationships among different views are not guaranteed during the learning process. In this paper, we propose a novel unsupervised multi-view representation learning model termed as Cross-View Equivariant Auto-Encoder (CVE-AE), which jointly conducts data reconstruction with view-specific autoencoder for information preservation within each view, and transformation reconstruction with transformation decoder for correlations preservation across different views. Accordingly, the generalization ability of our model is promoted due to the preserved intra-view intrinsic information and underlying inter-view relationships. We conduct extensive experiments on real-world datasets, and the proposed model achieves superior performance over stateof-the-art unsupervised representation learning methods.

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“…Automatically approximating samples in high-dimensional ambient space by a union of low-dimensional linear subspaces is considered to be a crucial task in computer vision [9], [23], [24], [25], [26]. In this section, we review the related contributions in the following three aspects, i.e., self-representation calculation, estimating the number of clusters and hyper-graph clustering.…”

Section: Related Workmentioning

confidence: 99%

Simultaneous Subspace Clustering and Cluster Number Estimating Based on Triplet Relationship

Liang

Yang

Cheng

et al. 2019

IEEE Trans. on Image Process.

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In this paper we propose a unified framework to simultaneously discover the number of clusters and group the data points into them using subspace clustering. Real data distributed in a high-dimensional space can be disentangled into a union of low-dimensional subspaces, which can benefit various applications. To explore such intrinsic structure, stateof-the-art subspace clustering approaches often optimize a selfrepresentation problem among all samples, to construct a pairwise affinity graph for spectral clustering. However, a graph with pairwise similarities lacks robustness for segmentation, especially for samples which lie on the intersection of two subspaces. To address this problem, we design a hyper-correlation based data structure termed as the triplet relationship, which reveals high relevance and local compactness among three samples. The triplet relationship can be derived from the self-representation matrix, and be utilized to iteratively assign the data points to clusters. Three samples in each triplet are encouraged to be highly correlated and are considered as a meta-element during clustering, which show more robustness than pairwise relationships when segmenting two densely distributed subspaces. Based on the triplet relationship, we propose a unified optimizing scheme to automatically calculate clustering assignments. Specifically, we optimize a model selection reward and a fusion reward by simultaneously maximizing the similarity of triplets from different clusters while minimizing the correlation of triplets from same cluster. The proposed algorithm also automatically reveals the number of clusters and fuses groups to avoid oversegmentation. Extensive experimental results on both synthetic and real-world datasets validate the effectiveness and robustness of the proposed method.

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Flexible Multi-View Dimensionality Co-Reduction

Cited by 126 publications

References 38 publications

Extraction of dynamic functional connectivity from brain grey matter and white matter for MCI classification

Extraction of dynamic functional connectivity from brain grey matter and white matter for MCI classification

Cross-View Equivariant Auto-Encoder

Simultaneous Subspace Clustering and Cluster Number Estimating Based on Triplet Relationship

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