This paper proposes a framework that combines neighbor embedding projections with rank-based manifold learning through rank aggregation, which can improve retrieval effectiveness in several scenarios, particularly when the baseline representation struggles to achieve high precision values.
V. Kawai, G. Leticio, L. Valem et al.· Journal of the Brazilian Com...· 0 citations
Experiments show that SCOSS is competitive with baselines, often the best choice for SGC, and enables favorable trade-offs between accuracy and efficiency, and on the fine-grained dataset, SGC with SCOSS outperforms SVMs when using fewer labeled samples.
GRaCE surpasses RaDE and Original Features across diverse datasets, including textual and image collections, excelling in retrieval, classification, and clustering tasks, considering state-of-the-art Transformer models as feature descriptors and Graph Convolutional Networks models in classification tasks.
Thiago César Castilho Almeida, G. Leticio, L. P. Valem et al.· IEEE International Joint Con...· 1 citation· ⚡1
Experimental results reveal that leveraging LLMs for graph refinement can improve classification accuracy, particularly for kNN graphs and some backbones, particularly for kNN graphs and some backbones.
Camila Piscioneri Magalhães, L. Valem· arXiv.org· 0 citations
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