Feature Extraction for Incomplete Data via Low-rank Tucker Decomposition

Qiquan Shi, Yiu Ming CHEUNG, Qibin Zhao

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

3 Citations (Scopus)

Abstract

Extracting features from incomplete tensors is a challenging task which is not well explored. Due to the data with missing entries, existing feature extraction methods are not applicable. Although tensor completion techniques can estimate the missing entries well, they focus on data recovery and do not consider the relationships among tensor samples for effective feature extraction. To solve this problem of feature extraction for incomplete data, we propose an unsupervised method, TDVM, which incorporates low-rank T ucker D ecomposition with feature V ariance M aximization in a unified framework. Based on Tucker decomposition, we impose nuclear norm regularization on the core tensors while minimizing reconstruction errors, and meanwhile maximize the variance of core tensors (i.e., extracted features). Here, the relationships among tensor samples are explored via variance maximization while estimating the missing entries. We thus can simultaneously obtain lower-dimensional core tensors and informative features directly from observed entries. The alternating direction method of multipliers approach is utilized to solve the optimization objective. We evaluate the features extracted from two real data with different missing entries for face recognition tasks. Experimental results illustrate the superior performance of our method with a significant improvement over the state-of-the-art methods.

Original languageEnglish
Title of host publicationMachine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2017, Proceedings
EditorsMichelangelo Ceci, Saso Dzeroski, Celine Vens, Ljupco Todorovski, Jaakko Hollmen
PublisherSpringer Verlag
Pages564-581
Number of pages18
ISBN (Print)9783319712482
DOIs
Publication statusPublished - 2017
EventEuropean Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2017 - Skopje, Macedonia, The Former Yugoslav Republic of
Duration: 18 Sep 201722 Sep 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10534 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceEuropean Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2017
Country/TerritoryMacedonia, The Former Yugoslav Republic of
CitySkopje
Period18/09/1722/09/17

Scopus Subject Areas

  • Theoretical Computer Science
  • Computer Science(all)

User-Defined Keywords

  • Feature extraction
  • Low-rank tucker decomposition
  • Missing data
  • Variance maximization

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