Coupled support tensor machine classification for multimodal neuroimaging data
Coupled support tensor machine classification for multimodal neuroimaging data
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DOI：
10.1002/sam.11587
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发表时间：
2022-01
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影响因子：
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通讯作者：
L. Peide;Seyyid Emre Sofuoglu;T. Maiti;Selin Aviyente
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文献类型：
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作者：
L. Peide;Seyyid Emre Sofuoglu;T. Maiti;Selin Aviyente
多模态数据出现在各种应用中，其中从多个传感器和不同成像模式获取有关同一现象的信息。从多模态数据中学习在机器学习和统计研究中具有很大的兴趣，因为它提供了在模态之间捕获互补信息的可能性。多模态建模有助于解释异构数据源之间的相互依存关系，发现可能无法从单一模态获得的新见解，并改进决策。近年来，在多模态数据融合中引入了耦合矩阵-张量分解，以联合估计潜在因素并识别潜在因素之间复杂的相互依存关系。然而，以往关于矩阵-张量因子耦合的研究大多集中在无监督学习上，而利用联合估计的潜在因子进行监督学习的研究很少。本文研究了多模态张量数据分类问题。提出了一种基于高级耦合矩阵-张量分解联合估计的潜在因子的耦合支持张量机（C‐STM）。C - STM将单个和共享的潜在因素与多个核相结合，并估计耦合矩阵-张量数据的最大边际分类器。C‐STM的分类风险收敛于最优贝叶斯风险，使其成为统计上一致的规则。C‐STM通过模拟研究以及同时分析脑电与功能磁共振成像数据进行验证。经验证据表明，C - STM可以利用来自多个来源的信息，并提供比传统单模分类器更好的分类性能。
Multimodal data arise in various applications where information about the same phenomenon is acquired from multiple sensors and across different imaging modalities. Learning from multimodal data is of great interest in machine learning and statistics research as this offers the possibility of capturing complementary information among modalities. Multimodal modeling helps to explain the interdependence between heterogeneous data sources, discovers new insights that may not be available from a single modality, and improves decision‐making. Recently, coupled matrix–tensor factorization has been introduced for multimodal data fusion to jointly estimate latent factors and identify complex interdependence among the latent factors. However, most of the prior work on coupled matrix–tensor factors focuses on unsupervised learning and there is little work on supervised learning using the jointly estimated latent factors. This paper considers the multimodal tensor data classification problem. A coupled support tensor machine (C‐STM) built upon the latent factors jointly estimated from the advanced coupled matrix–tensor factorization is proposed. C‐STM combines individual and shared latent factors with multiple kernels and estimates a maximal‐margin classifier for coupled matrix–tensor data. The classification risk of C‐STM is shown to converge to the optimal Bayes risk, making it a statistically consistent rule. C‐STM is validated through simulation studies as well as a simultaneous analysis on electroencephalography with functional magnetic resonance imaging data. The empirical evidence shows that C‐STM can utilize information from multiple sources and provide a better classification performance than traditional single‐mode classifiers.