Semi-supervised co-selection: Features and instances by a weighting approach

Makkhongkaew, Raywat; Benabdeslem, Khalid; Elghazel, Haytham

doi:10.1109/ijcnn.2016.7727645

Search citation statements

Order By: Relevance

Paper Sections

Select...

B Instance Selection1

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2020

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

(1 citation statement)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It can be found that these OED series criteria aim to select the representative instances. Moreover, several methods have also been developed in supervised scenario [48]- [51] and semisupervised case [52], [53].…”

Section: B Instance Selectionmentioning

confidence: 99%

Unsupervised Dual Learning for Feature and Instance Selection

Ren

Zhou

et al. 2020

IEEE Access

View full text Add to dashboard Cite

Feature selection and instance selection are dual operations on a data matrix. Feature selection aims at selecting a subset of relevant and informative features from original feature space, while instance selection at identifying a subset of informative and representative instances. Most of previous studies address these two problems separately, such that irrelevant features (resp. outliers) may mislead the process of instance (resp. feature) selection. In this paper, we address the problem by doing feature and instance selection simultaneously. We propose a novel unified framework, which chooses instances and features simultaneously, such that 1)all the data can be reconstructed from the selected instances and features and 2) the global structure which is characterized by the sparse reconstruction coefficient is preserved. Experimental results on several benchmark data sets demonstrate the effectiveness of our proposed method.

show abstract