Partial multi-labelLabel disambiguationLow-rankSparseMulti-view partial multi-label (MVPML) problems are often encountered in our real life. Each training sample described by multi-view is associated with a set of candidate labels, including multiple ground-truth labels and noisy labels. Currently, ...
Partial multi-label learning is of great significant interest due to accurate supervision is difficult to be obtained. Recently, multi-view learning has be
As shown in Table4, we evaluated the predictive accuracy of our model using maximumF_{1}-score (F_{max}), one of the metrics used in CAFA evaluations. Consistent with previous studies, it is evident that the machine learning (ML) methods (DeepGOCNN, DeepGOMLP and GO-LTR) outperform t...
Incomplete multi-view learning under label shift. IEEE Transactions on Image Processing, 2023, 32: 3702–3716 Article Google Scholar Fu Y, Hospedales T M, Xiang T, Gong S. Transductive multi-view zero-shot learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(11...
The second step is to solve the multi-label missing label learning problem by using the label matrix completion method in combination with the kernel extreme learning machine classifier. The kernel extreme learning machine can effectively enhance the robustness of the algorithm to missing labels. The...
2022Foundations and Recent Trends in Multimodal Machine Learning: Principles, Challenges, and Open QuestionsArxiv 2021Survey on Deep Multi-modal Data Analytics: Collaboration, Rivalry, and FusionTOMM 2021Deep Multi-view Learning Methods: A ReviewNeurocom ...
In [30], a Multimodal Deep Botlzman Machine (DBM) was proposed to jointly model the distribution over two views. In [3], Deep Canonical Correlation Analysis (DC- CA) was proposed to learn complex nonlinear transforma- tions for each of the two views so that the resulting rep- re...
1. Introduction In the coming decade, dense 3D data acquisition of ob- jects is likely to become one of the most important problems in computer vision and industrial machine vision. More- over, it can be helpful for a wide range of other cutting-edge scientific disciplines such as...
Multi-view learning refers to the machine learning paradigm where data is represented by multiple feature sets. Multi-view data are both consistent and complementary, and the same object can be described from different perspectives. Each view of multi-view data contains both unique and shared inform...
3D human pose estimation aims to reconstruct the human skeleton of all the individuals in a scene by detecting several body joints. The creation of accurate and efficient methods is required for several real-world applications including animation, human–robot interaction, surveillance systems or sports...