Sparse multi-label bilinear embedding on stiefel manifolds

Yang LIU*, Guohua Dong, Zhonglei Gu

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference contributionpeer-review


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.

Original languageEnglish
Title of host publicationFoundations of Intelligent Systems - 24th International Symposium, ISMIS 2018, Proceedings
EditorsNathalie Japkowicz, George A. Papadopoulos, Michelangelo Ceci, Zbigniew W. Ras, Jiming Liu
PublisherSpringer Verlag
Number of pages11
ISBN (Print)9783030018504
Publication statusPublished - 2018
Event24th International Symposium on Methodologies for Intelligent Systems, ISMIS 2018 - Limassol, Cyprus
Duration: 29 Oct 201831 Oct 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11177 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference24th International Symposium on Methodologies for Intelligent Systems, ISMIS 2018

Scopus Subject Areas

  • Theoretical Computer Science
  • Computer Science(all)

User-Defined Keywords

  • Column-orthonormal constraints
  • Dimensionality reduction
  • norm regularization
  • Second-order tensor
  • Sparse multi-label bilinear embedding
  • Stiefel manifolds


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