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Unsupervised feature selection based on variance–covariance subspace distance

S. Karami, F. Saberi-Movahed, P. Tiwari, P. Marttinen, and S. Vahdati. Neural Networks, (2023)
DOI: https://doi.org/10.1016/j.neunet.2023.06.018

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Adaptive Margin Ranking Loss for Knowledge Graph Embeddings via a Correntropy Objective FunctionM. Nayyeri, X. Zhou, S. Vahdati, H. Yazdi, and J. Lehmann. ArXiv, (2019)"Why do you cite?" An investigation on Citation Intents and Decision-Making Classification ProcessesL. Paolini, S. Vahdati, A. Di Iorio, R. Wardenga, I. Heibi, and S. Peroni. (2024)Semantic representation of physics research dataA. Say, S. Fathalla, S. Vahdati, J. Lehmann, and S. Auer. Proceedings of the 12th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, SCITEPRESS - Science and Technology Publications, (2020)SimE: A Knowledge Graph Embedding Model to Encode Self-Similar Structures Through Algebraic and Geometric TransformationsK. Amouzouvi, Y. Chudasama, D. Purohit, A. Rivas, B. Song, J. Lehmann, S. Vahdati, and M. Vidal. IEEE Access, (2025)Fantastic knowledge graph embeddings and how to find the right space for themM. Nayyeri, C. Xu, S. Vahdati, N. Vassilyeva, E. Sallinger, H. Yazdi, and J. Lehmann. Lecture Notes in Computer Science, Springer International Publishing, Cham, (2020)Utilizing transformers on OCT imagery and metadata for treatment response prediction in macular edema patientsS. Vahdati, D. Nagaraj, M. Bryan, S. Moazemi, S. Gründer-Fahrer, and M. Martin. Lecture Notes in Computer Science, Springer Nature Switzerland, Cham, (2023)Let the margin SlidE$\pm$ for knowledge graph embeddings via a correntropy objective functionM. Nayyeri, X. Zhou, S. Vahdati, R. Izanloo, H. Yazdi, and J. Lehmann. 2020 International Joint Conference on Neural Networks (IJCNN), IEEE, (July 2020)Embedding-based recommendations on scholarly knowledge graphsM. Nayyeri, S. Vahdati, X. Zhou, H. Shariat Yazdi, and J. Lehmann. The Semantic Web, Springer International Publishing, Cham, (2020)Ontology design for pharmaceutical research outcomesZ. Say, S. Fathalla, S. Vahdati, J. Lehmann, and S. Auer. Digital Libraries for Open Knowledge, Springer International Publishing, Cham, (2020)Large Language Models for Scientific Question Answering: An Extensive Analysis of the SciQA BenchmarkJ. Lehmann, A. Meloni, E. Motta, F. Osborne, D. Recupero, A. Salatino, and S. Vahdati. ESWC 2024, 14664, page 199--217. Cham, Springer, (2024)

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Unsupervised feature selection based on variance–covariance subspace distance

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copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Unsupervised feature selection based on variance–covariance subspace distance

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Unsupervised feature selection based on variance–covariance subspace distance