Multi- View Learning Based on Common and Special Features in Multi- Task Scenarios

Lu, Run-kun; Zuo, Xin; Liu, Jianwei; Lian, Si-ming

doi:10.23919/chicc.2018.8482783

Cited by 2 publications

(3 citation statements)

References 19 publications

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“…Therefore, a generative model should be proposed to solve this problem, and we can also learn from some popular generative methods, such as variational auto-encoders and generative adversarial nets [46,47]. Besides, in this paper and some other matrix factorization methods [5,12,25,34,48,49],…”

Section: Discussionmentioning

confidence: 99%

“…However, some new research works begin to realize that both consistency and complementarity are significant in such learning problem. For example, [34] proposed a joint matrix factorization algorithm, and [35] proposed a deep learning based model to find shared and unified features of multiple views in each task. Additionally, [36] proposed an online model using lifelong learning to find views' representations of the coming task.…”

Section: Multi-view Learningmentioning

confidence: 99%

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Multi-view representation learning in multi-task scene

Liu

Lian

et al. 2019

Neural Comput & Applic

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Over recent decades have witnessed considerable progress in whether multi-task learning or multiview learning, but the situation that consider both learning scenes simultaneously has received not too much attention. How to utilize multiple views' latent representation of each single task to improve each learning task's performance is a challenge problem. Based on this, we proposed a novel semi-supervised algorithm, termed as Multi-Task Multi-View learning based on Common and Special Features (MTMVCSF). In general, multi-views are the different aspects of an object and every view includes the underlying common or special information of this object. As a consequence, we will mine multiple views' jointly latent factor of each learning task which consists of each view's special feature and the common feature of all views. By this way, the original multi-task multi-view data has degenerated into multi-task data, and exploring the correlations among multiple tasks enables to make an improvement on the performance of learning algorithm. Another obvious advantage of this approach is that we get latent representation of the set of unlabeled instances by the constraint of regression task with labeled instances. The performance of classification and semi-supervised clustering task in these latent representations perform obviously better than it in raw data. Furthermore, an anti-noise multi-task multi-view algorithm called AN-MTMVCSF is proposed, which has a strong adaptability to noise labels. The effectiveness of these algorithms is proved by a series of well-designed experiments on both real world and synthetic data.

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Section: Discussionmentioning

confidence: 99%

Section: Multi-view Learningmentioning

confidence: 99%

Multi-view representation learning in multi-task scene

Liu

Lian

et al. 2019

Neural Comput & Applic

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“…In a more general point of view, the algorithms developed here are related to the multitask learning area (Caruana, 1997), which also focuses on jointly learning multiple related tasks, with a number of successful applications (Liu et al ., 2015, 2016). Thus, in case, one models such tasks using a concept learning setting, the algorithms developed here could also be applied to tackle the same type of problems handled by that area.…”

Section: Related Workmentioning

confidence: 99%

Learning multiple concepts in description logic through three perspectives

Melo¹,

Revoredo²,

Paes³

2021

The Knowledge Engineering Review

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An ontology formalises a number of dependent and related concepts in a domain, encapsulated as a terminology. Manually defining such terminologies is a complex, time-consuming and error-prone task. Thus, there is great interest for strategies to learn terminologies automatically. However, most of the existing approaches induce a single concept definition at a time, disregarding dependencies that may exist among the concepts. As a consequence, terminologies that are difficult to interpret may be induced. Thus, systems capable of learning all concepts within a single task, respecting their dependency, are essential for reaching concise and readable ontologies. In this paper, we tackle this issue presenting three terminology learning strategies that aim at finding dependencies among concepts, before, during or after they have been defined. Experimental results show the advantages of regarding the dependencies among the concepts to achieve readable and concise terminologies, compared to a system that learns a single concept at a time. Moreover, the three strategies are compared and analysed towards discussing the strong and weak points of each one.

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Multi- View Learning Based on Common and Special Features in Multi- Task Scenarios

Cited by 2 publications

References 19 publications

Multi-view representation learning in multi-task scene

Multi-view representation learning in multi-task scene

Learning multiple concepts in description logic through three perspectives

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