---
title: "GSPL: A Succinct Kernel Model for Group-Sparse Projections Learning of Multiview Data"
authors:
  - "Danyang Wu"
  - "Jin Xu"
  - "Xia Dong"
  - "Meng Liao"
  - "Rong Wang"
  - "Feiping Nie"
  - "Xuelong Li"
doi: "10.24963/ijcai.2021/438"
url: "https://ijcai.org/proceedings/2021/438"
pdf: "https://ijcai.org/proceedings/2021/0438.pdf"
venue: "Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence"
year: 2021
pages: "3185-3191"
track: "Main Track"
keywords:
  - "Machine Learning: Learning Sparse Models"
  - "Machine Learning: Unsupervised Learning"
  - "Machine Learning: Multi-instance; Multi-label; Multi-view learning"
---

# GSPL: A Succinct Kernel Model for Group-Sparse Projections Learning of Multiview Data

Danyang Wu, Jin Xu, Xia Dong, Meng Liao, Rong Wang, Feiping Nie, Xuelong Li

*Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence*, pages 3185-3191. DOI: [10.24963/ijcai.2021/438](https://doi.org/10.24963/ijcai.2021/438)

## Abstract

This paper explores a succinct kernel model for Group-Sparse Projections Learning (GSPL), to handle multiview feature selection task completely. Compared to previous works, our model has the following useful properties: 1) Strictness: GSPL innovatively learns group-sparse projections strictly on multiview data via ‘2;0-norm constraint, which is different with previous works that encourage group-sparse projections softly. 2) Adaptivity: In GSPL model, when the total number of selected features is given, the numbers of selected features of different views can be determined adaptively,
which avoids artificial settings. Besides, GSPL can capture the differences among multiple views adaptively, which handles the inconsistent problem among different views. 3) Succinctness: Except for the intrinsic parameters of projection-based feature selection task, GSPL does not bring extra parameters, which guarantees the applicability in practice. To solve the optimization problem involved in GSPL, a novel iterative algorithm is proposed with rigorously theoretical guarantees. Experimental results demonstrate the superb performance of GSPL on synthetic and real datasets.