Zobrazeno 1 - 10
of 22
pro vyhledávání: '"Wenzhang Zhuge"'
Publikováno v:
IEEE Access, Vol 8, Pp 74864-74874 (2020)
Multi-instance learning (MIL) plays an important role in many real applications, such as image recognition and text classification. The instance-based approach selects instances in each bag to train and has drawn significant attention recently. Howev
Externí odkaz:
https://doaj.org/article/70bba6c0a4b24b01b0ad85a9a667d09d
Publikováno v:
IEEE Access, Vol 8, Pp 99820-99831 (2020)
Traditional multi-view learning usually assumes each instance appears in all views. However, in real-world applications, it is not an uncommon case that a number of instances suffer from some view samples missing. How to effectively cluster this kind
Externí odkaz:
https://doaj.org/article/8d914582de014ada9d3aa4dc328e38f0
Publikováno v:
PLoS ONE, Vol 12, Iss 5, p e0176769 (2017)
In many computer vision and machine learning applications, the data sets distribute on certain low-dimensional subspaces. Subspace clustering is a powerful technology to find the underlying subspaces and cluster data points correctly. However, tradit
Externí odkaz:
https://doaj.org/article/7e345f18526e48c19218c3e60a2b0b70
Publikováno v:
IEEE Transactions on Knowledge and Data Engineering. 35:877-890
The scarcity of labels is common and great challenge in traditional supervised learning. Semi-supervised learning (SSL) leverages unlabeled samples to alleviate the absence of label information. Similar with annotation, label proportion is another ty
Publikováno v:
IEEE Transactions on Knowledge and Data Engineering. 34:3826-3840
Partial multi-view clustering has attracted various attentions from diverse fields. Most existing methods adopt separate steps to obtain unified representations and extract clustering indicators. This separate manner prevents two learning processes t
Publikováno v:
IEEE Transactions on Cybernetics. 51:5156-5169
Multi-instance learning (MIL) has been extensively applied to various real tasks involving objects with bags of instances, such as in drugs and images. Previous studies on MIL assume that data are entirely complete. However, in many real tasks, the i
Publikováno v:
Neurocomputing. 443:106-116
In real applications, to deal with incomplete multi-view data, incomplete multi-view learning has experienced rapid development in recent years. Among various incomplete multi-view learning methods, a considerable number of methods were developed wit
Publikováno v:
Neurocomputing. 436:12-21
Label Distribution Learning (LDL) is a new learning paradigm to describe supervision as probability distribution and has been successfully applied in many real-world scenarios in recent years. In LDL applications, the availability of a large amount o
Publikováno v:
Neurocomputing. 410:317-327
In this paper, we study the error correcting output codes for multi-label classification problems. Imbalance problem is very common in multi-label task and it has not been effectively solved yet. In previous works, base classifiers are learned from t
Publikováno v:
IEEE Access, Vol 8, Pp 99820-99831 (2020)
Traditional multi-view learning usually assumes each instance appears in all views. However, in real-world applications, it is not an uncommon case that a number of instances suffer from some view samples missing. How to effectively cluster this kind