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Sparse Multi-label Bilinear Embedding on Stiefel Manifolds

  • Yang LiuEmail author
  • Guohua Dong
  • Zhonglei Gu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11177)

Abstract

Dimensionality reduction plays an important role in various machine learning tasks. In this paper, we propose a novel method dubbed Sparse Multi-label bILinear Embedding (SMILE) on Stiefel manifolds for supervised dimensionality reduction on multi-label data. Unlike the traditional multi-label dimensionality reduction algorithms that work on the vectorized data, the proposed SMILE directly takes the second-order tensor data as the input, and thus characterizes the spatial structure of the tensor data in an efficient way. Differentiating from the existing tensor-based dimensionality reduction methods that perform the eigen-decomposition in each iteration, SMILE utilizes a gradient ascent strategy to optimize the objective function in each iteration, and thus is more efficient. Moreover, we introduce column-orthonormal constraints to transformation matrices to eliminate the redundancy between the projection directions of the learned subspace and add an \(L_1\)-norm regularization term to the objective function to enhance the interpretability of the learned subspace. Experiments on a standard image dataset validate the effectiveness of the proposed method.

Keywords

Sparse multi-label bilinear embedding Dimensionality reduction Second-order tensor Column-orthonormal constraints Stiefel manifolds \(L_1\)-norm regularization 

Notes

Acknowledgment

This work was supported in part by the National Natural Science Foundation of China (NSFC) under Grant 61503317, in part by the General Research Fund (GRF) from the Research Grant Council (RGC) of Hong Kong SAR under Project HKBU12202417, and in part by the SZSTI Grant with the Project Code JCYJ20170307161544087.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Department of Computer ScienceHong Kong Baptist UniversityKowloonHong Kong SAR
  2. 2.HKBU Institute for Research and Continuing EducationShenzhenPeople’s Republic of China
  3. 3.Songshan Lake TechX InstituteDongguanPeople’s Republic of China

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