Multi-label Learning with Label Enhancement

Shao, Renfan; Xu, Ning; Geng, Xin

doi:10.1109/icdm.2018.00059

Cited by 24 publications

(12 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Shao et al [12] proposed an effective MLL method called label-enhanced MLL, which is based on label enhancement. Through this approach, problems were developed by combining numerical label and labelenhanced regression into a unified framework, in which numerical labels and predictive models are learned jointly.…”

Section: Multi-label Learning With Label Enhancementmentioning

confidence: 99%

LTSA-LE: A local tangent space alignment label enhancement algorithm

Tan

Liu

et al. 2021

Tinshhua Sci. Technol.

View full text Add to dashboard Cite

According to smoothness assumption, local topological structure can be shared between feature and label manifolds. This study proposes a new algorithm based on Local Tangent Space Alignment (LTSA) to implement the label enhancement process. In general, we first establish a learning model for feature extraction in label space and use a feature extraction method of LTSA to guide the reconstruction of label manifolds. Then, we establish an unconstrained optimization model based on the optimal theory presented in this paper. The model is suitable for solving problems with a large number of sample points. Finally, the experiment results show that the algorithm can effectively improve the training speed and multilabel dataset prediction accuracy.

show abstract

Section: Multi-label Learning With Label Enhancementmentioning

confidence: 99%

LTSA-LE: A local tangent space alignment label enhancement algorithm

Tan

Liu

et al. 2021

Tinshhua Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…In the context of multi-label classification, label importance was introduced by Geng [1], with which label distribution is inferred with supervised learning. Later, Shao et al [2] proposed an unsupervised approach, with which numerical label distribution is inferred under a constraint of given binary labels, assuming the similarity between the topology of input features and that of the label distributions. Tag feature extraction was also investigated in the context of tag recommendation, with which proper tags are recommended to annotators.…”

Section: Related Workmentioning

confidence: 99%

“…We adopted the MovieLens dataset. 2 The dataset has transaction data of user ratings to movies, which consists of 610 users, 9724 items, and 100,836 ratings. We extracted samples rated above 3.5 as positive samples.…”

Section: Datasetmentioning

confidence: 99%

“…In the context of multi-labeled classification, Geng [1] proposed a label enhancement approach for binarized sample labels. Supervised learning for label distribution was developed in the earlier implementation, and a later unsupervised approach was proposed by Shao et al [2]. They used a similarity assumption between the topology of input features and that of the binary label distributions.…”

Section: Introductionmentioning

confidence: 99%

“…As a result, we found that this preprocessing improved the accuracy by about 3-5 percent. More rigorous experiments and evaluation against a broader range of models are left to future research 2. https ://group lens.org/datas ets/movie lens/.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Unsupervised Qualitative Scoring for Binary Item Features

Ichikawa

Tamano

2020

Data Sci. Eng.

View full text Add to dashboard Cite

Binary features, such as categories, keywords, or tags, are widely used to describe product properties. However, these features are incomplete in that they do not contain several aspects of numerical information. The qualitative score of tags is widely used to describe which product is better in terms of the given property. For example, in a restaurant navigation site, properties such as mood, dishes, and location are given in the form of numerical values, representing the goodness of each aspect. In this paper, we propose a novel approach to estimate the qualitative score from the binary features of products. Based on a natural assumption that an item with a better property is more popular among users who prefer that property, in short, “experts know best,” we introduce both discriminative and generative models with which user preferences and item qualitative scores are inferred from user--item interactions. We constrain the space of the item qualitative score by item binary features so that the score of each item and tag can only have nonzero values when the item has the corresponding tag. This approach contributes to resolving the following difficulties: (1) no supervised data for the score estimation, (2) implicit user purpose, and (3) irrelevant tag contamination. We evaluate our models by using two artificial datasets and two real-world datasets of movie and book ratings. In the experiment, we evaluate the performances of our model under sparse transaction and noisy tag settings by using two artificial datasets. We also evaluate our models’ resolution for irrelevant tags using the real-world dataset of movie ratings and observe that our models outperform a baseline model. Finally, tag rankings obtained from the real-world datasets are compared with a baseline model.

show abstract

Robust multi-label feature selection with shared label enhancement

Gao

2022

Knowl Inf Syst

View full text Add to dashboard Cite

In real-world applications, multi-label feature selection has been widely attract considerable attention due to the importance of multi-label data. However, previous methods do not fully consider the relationship between the feature set and the multi-label set but devote attention to either of them. In addition, the existence of irrelevant and redundant information in the feature set and the multi-label set makes previous methods obtain inaccurate results. Moreover, traditional multi-label learning utilizes logical labels to estimate the relevance between the feature set and the label set so that the importance of labels can not be well-reflected. To deal with these issues, we propose a novel robust multi-label feature selection method named RLEFS in this paper. RLEFS utilizes a shared space by mapping patterns to excavate semantic similarity structure in features and labels. Besides, we reconstruct the label space to obtain numerical labels by a label enhancement regularization term during mining semantic similarity structure process. Furthermore, the local and global structures are considered to ensure effective information can be captured as fully as possible during feature selection process. Finally, we integrate the above terms into one joint learning framework, and then a simple yet effective optimization method with provable convergence is proposed to solve the above problems. Experimental results on multiple data sets show that the superiority of the proposed method.

show abstract

Multi-label Learning with Label Enhancement

Cited by 24 publications

References 21 publications

LTSA-LE: A local tangent space alignment label enhancement algorithm

LTSA-LE: A local tangent space alignment label enhancement algorithm

Unsupervised Qualitative Scoring for Binary Item Features

Robust multi-label feature selection with shared label enhancement

Contact Info

Product

Resources

About