9 accepted papers
Manifold clustering has demonstrated strong capability in capturing complex data structures and has been widely studied in cluster analysis. However, many existing methods mainly focus on combining K-means with manifold learning, while overlooking the consistency between data structures and clusteri…
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 m
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 th
Large language models (LLMs) have shown promise in providing scalable mental health support, while evaluating their counseling capability remains crucial to ensure both efficacy and safety. Existing evaluations are limited by the static assessment that focuses on knowledge tests, the single perspect
Signed networks can reflect more complex connections through positive and negative edges, and cost-effective signed network sketching can significantly benefit an important link sign prediction task in the era of big data. Existing signed network embedding algorithms mainly learn node representation…
The goal of RGB-T tracking is to enhance the accuracy and robustness by leveraging the complementary features of RGB and TIR modalities in complex scenarios. Previous methods have overlooked the power of semantic features in extracting valuable information from different modalities and improving int
Alzheimer’s Disease (AD), characterized by significant cognitive and functional impairment, necessitates the development of early detection techniques. Traditional diagnostic practices, such as cognitive assessments and biomarker analysis, are often invasive and costly. Deep learning-based approache…
Multi-label text classification (MLTC) aims to assign multiple labels to a given text. Previous works have focused on text representation learning and label correlations modeling using pre-trained language models (PLMs). However, studies have shown that PLMs generate word frequency-oriented text re…
Chinese spelling check (CSC) detects and corrects spelling errors in Chinese texts. Previous approaches have combined character-level phonetic and graphic information, ignoring the importance of segment-level information. According to our pilot study, spelling errors are always associated with incor…