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 proceedingpeer-review

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.

Original languageEnglish
Title of host publicationFoundations of Intelligent Systems
Subtitle of host publication24th International Symposium, ISMIS 2018, Limassol, Cyprus, October 29–31, 2018, Proceedings
EditorsMichelangelo Ceci, Nathalie Japkowicz, Jiming Liu, George A. Papadopoulos, Zbigniew W. Raś
Place of PublicationCham
PublisherSpringer
Pages203-213
Number of pages11
Edition1st
ISBN (Electronic)9783030018511
ISBN (Print)9783030018504
DOIs
Publication statusPublished - 7 Oct 2018
Event24th International Symposium on Methodologies for Intelligent Systems, ISMIS 2018 - Limassol, Cyprus
Duration: 29 Oct 201831 Oct 2018
https://link.springer.com/book/10.1007/978-3-030-01851-1

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume11177
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349
NameLecture Notes in Artificial Intelligence
ISSN (Print)2945-9133
ISSN (Electronic)2945-9141
NameISMIS: International Symposium on Methodologies for Intelligent Systems

Conference

Conference24th International Symposium on Methodologies for Intelligent Systems, ISMIS 2018
Country/TerritoryCyprus
CityLimassol
Period29/10/1831/10/18
Internet address

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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