arXiv:2607. 05464v1 Announce Type: cross Abstract: The success of categorical data clustering generally much relies on the distance metric that measures the dissimilarity degree between two objects.
By Yiqun Zhang, Yiu-ming Cheung
The paper introduces a method for combining heterogeneous, allied datasets—datasets that share the same class labels but have disjoint objects and largely distinct feature spaces—into a single unified feature space. By applying matrix completion to this merged space, the authors create a unified dataset that enables knowledge transfer between the original datasets. Experiments across multiple dataset pairs and classifiers show that models trained on the unified representation consistently outperform those trained separately on each dataset.
By Girish Keshav Palshikar
The paper investigates the effect of using semantic positive pairs—different instances of the same class—in self‑supervised visual representation learning. By creating matched ImageNet‑1K subsets of augmented pairs and manually curated semantic pairs, the authors compare contrastive and non‑contrastive SSL methods under identical training conditions. Across transfer learning and object detection tasks, semantic‑pair pretraining consistently outperforms augmented‑pair pretraining, with contrastive methods like SimCLR showing the largest gains, indicating that semantic pairs foster additional invariances beyond standard augmentations.
By Mohammad Alkhalefi, Georgios Leontidis, Mingjun Zhong
arXiv:2605. 09420v2 Announce Type: replace-cross Abstract: In this study, we tackle Generalized Category Discovery (GCD) via a Relational Retrieval perspective, explicitly coupling labeled and unlabeled data through bidirectional knowledge transfer.
By Yulin Xu, Chunqi Guo, Yuanzhen Shuai, Jianyuan Ni
arXiv:2603. 29488v2 Announce Type: replace Abstract: Cosine similarity is often used to measure the similarity of vector representations of neural network models.
By Beatrix M. G. Nielsen, Andreas Grivas
The paper examines convergence problems in Relational Concept Analysis (RCA) when applied to AOC-posets instead of full concept lattices. It explains why RCA’s iterative process may fail to converge in the AOC-poset setting, identifies conditions that can still guarantee convergence, and proposes a convergent variant that preserves the AOC-poset structure by never removing relational attributes. The study also discusses data transformations that can restore convergence.
By Xavier Dolques, Agn\`es Braud, Alain Gutierrez, Marianne Huchard, Florence Le Ber