The paper introduces Coupled Tensor‑Tensor Completion (CTTC), a new framework that incorporates side information in tensor form to enhance tensor completion tasks. CTTC leverages hidden connections among multimodal tensors and is grounded in distance metric learning and group theory. Experiments on the DTD and LINCS datasets show that CTTC outperforms existing methods such as HaLRTC, CTRC, Cell, and NTDDR in both runtime and root‑sum‑of‑squares error for drug effect prediction.
By Maryam Bagherian, Albert Hung, Ivo Dinov, Joshua Welch
The paper introduces Coupled Tensor‑Tensor Completion (CTTC), a new framework that incorporates side information in tensor form to enhance tensor completion tasks. CTTC leverages hidden connections among multimodal tensors and is grounded in distance metric learning and group theory. Experiments on the DTD and LINCS datasets show that CTTC outperforms existing methods such as HaLRTC, CTRC, Cell, and NTDDR in both run‑time and root‑sum‑of‑errors accuracy for predicting drug effects.
arXiv:2412. 07041v4 Announce Type: replace-cross Abstract: Recovering incomplete multidimensional tensor-structured data is a fundamental task in many real-world applications.
By Mengying Lei, Lijun Sun
arXiv:2607. 27507v1 Announce Type: new Abstract: Matrix factorisation is a fundamental tool for exploiting low-dimensional structure in high-dimensional data, with applications such as data compression, denoising, structure discovery, interpretable representation learning, and dimensionality reduction.
By Tingting Mu
arXiv:2506.04166v3 Announce Type: replace
Abstract: Nearest neighbor (NN) methods have re-emerged as competitive tools for matrix completion, offering strong empirical performance and recent theoreti...
By Caleb Chin, Aashish Khubchandani, Harshvardhan Maskara, Kyuseong Choi, Jacob Feitelberg, Albert Gong, Manit Paul, Tathagata Sadhukhan, Dwaipayan Saha, Anish Agarwal, Raaz Dwivedi
arXiv:2607. 20084v1 Announce Type: cross Abstract: Non--negative matrix factorization (NMF) has become an established dimensionality reduction technique for extracting latent structures from non--negative data and has found widespread applications in fields such as bioinformatics, text mining, image analysis, and recommender systems.
By Volkan Sevin\c{c}, Nikolas Kontemeniotis, Theodoros Perdikis, Michail Tsagris