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Yapeng Wang

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4 papers

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AAAI Conference 2026 Conference Paper

Maximizing Schatten-p Norm Regularization Toward Balance

Fangfang Li
Quanxue Gao
Yapeng Wang
Yu Duan
Yuzhuo Feng
Qin Li

The Schatten-p norm, as a class of structure-inducing norms based on singular values, has been widely used to enhance model low-rankness and representation capability due to its flexibility in structural modeling and favorable mathematical properties. However, its potential in cluster distribution modeling has long been overlooked. Therefore, we explore the potential of maximizing the Schatten-p norm as a regularization strategy specifically designed to achieve balanced clustering. This work is the first to investigate its effectiveness in promoting cluster balance. To be specific, maximizing Schatten-p norm effectively guides the assignment of data points, ensuring a more balanced distribution of samples across clusters. We have conducted an in-depth theoretical analysis and validated its effectiveness through extensive clustering experiments. Experimental results demonstrate that, compared to existing methods, this regularization term significantly improves clustering quality and obtain reasonable clustering.

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AAAI Conference 2026 Conference Paper

Unified View Extraction with Low-Rankness and Smoothness Fusion for Multi-View Subspace Clustering

Yapeng Wang
Quanxue Gao
Fangfang Li
Yu Yun
Ming Yang

Tensor-based multi-view subspace clustering (MVSC) has achieved significant success by capturing high-order inter-view correlations. However, existing approaches face two principal limitations. First, most methods either exclusively emphasize the inter-view low‑rankness (R) prior while neglecting the intra-view local smoothness (S) prior, or treat R and S as two separate regularizers—complicating joint optimization. Second, conventional tensor‑based methods impose only low‑rank constraints on the representation tensor, which limits their ability to simultaneously model consistency and complementary information. To address these issues, we propose a Unified View Extraction with Low‑Rankness and Smoothness Fusion (UVELRS) method. Our framework first extracts a consistent cross‑view representation and then constructs a tensor by stacking these representations. We introduce a novel tensor total variation Schatten-p norm that simultaneously encodes both R and S priors while offering flexible singular‑value control. This unified formulation effectively captures both high-order inter-view correlations and intra-view local smoothness. Extensive experiments on real‑world datasets demonstrate UVELRS's superior performance and robustness.

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YNIMG Journal 2009 Journal Article

Sustained and transient language control in the bilingual brain

Yapeng Wang
Patricia K. Kuhl
Chunhui Chen
Qi Dong

Bilingual speakers must have effective neural mechanisms to control and manage their two languages, but it is unknown whether bilingual language control includes different control components. Using mixed blocked and event-related designs, the present study explored the sustained and transient neural control of two languages during language processing. 15 Chinese–English bilingual speakers were scanned when they performed language switching tasks. The results showed that, compared to the single language condition, sustained bilingual control (mixed language condition) induced activation in the bilateral inferior frontal, middle prefrontal and frontal gyri (BA 45/46). In contrast, relative to the no switch condition, transient bilingual control (language switching condition) activated the left inferior parietal lobule (BA 2/40), superior parietal lobule (BA 7), and middle frontal gyrus (BA 11/46). Importantly, the right superior parietal activity correlated with the magnitude of the mixing cost, and the left inferior and superior parietal activity covaried with the magnitude of the asymmetric switching costs. These results suggest that sustained and transient language control induced differential lateral activation patterns, and that sustained and transient activities in the human brain modulate the behavioral costs during switching-related language control.

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YNIMG Journal 2007 Journal Article

Neural bases of asymmetric language switching in second-language learners: An ER-fMRI study

Yapeng Wang
Gui Xue
Chuansheng Chen
Feng Xue
Qi Dong

Using the ER-fMRI technique, the present study was designed to investigate the neural substrates of language switching among second-language learners. Twelve Chinese college students who were learning English were scanned when they performed language switching tasks (naming pictures in their first [L1, Chinese] and second [L2, English] languages according to response cues). Compared to non-switching conditions, language switching elicited greater activation in the right superior prefrontal cortex (BA9/10/32), left middle and superior frontal cortex (BA8/9/46), and right middle cingulum and caudate (BA11). When the direction of switching was considered, forward switching (from L1 to L2), but not backward switching (from L2 to L1), activated several brain regions related to executive functions (i. e. , bilateral frontal cortices and left ACC) relative to non-switching conditions. These results suggest that neural correlates of language switching differ depending on the direction of the switch and that there does not seem to be a specific brain area acting as a “language switch”.

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