Uncovering locally discriminative structure for feature analysis

Sen Wang*, Feiping Nie, Xiaojun Chang, Xue Li, Quan Z. Sheng, Lina Yao

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

6 Citations (Scopus)

Abstract

Manifold structure learning is often used to exploit geometric information among data in semi-supervised feature learning algorithms. In this paper, we find that local discriminative information is also of importance for semi-supervised feature learning. We propose a method that utilizes both the manifold structure of data and local discriminant information. Specifically, we define a local clique for each data point. The k-Nearest Neighbors (kNN) is used to determine the structural information within each clique. We then employ a variant of Fisher criterion model to each clique for local discriminant evaluation and sum all cliques as global integration into the framework. In this way, local discriminant information is embedded. Labels are also utilized to minimize distances between data from the same class. In addition, we use the kernel method to extend our proposed model and facilitate feature learning in a highdimensional space after feature mapping. Experimental results show that our method is superior to all other compared methods over a number of datasets.

Original languageEnglish
Title of host publicationMachine learning and knowledge discovery in databases
Subtitle of host publicationEuropean Conference, ECML PKDD 2016, Riva del Garda, Italy, September 19–23, 2016, proceedings, part I
EditorsPaolo Frasconi, Niels Landwehr, Giuseppe Manco, Jilles Vreeken
Place of PublicationCham
PublisherSpringer, Springer Nature
Pages281-295
Number of pages15
ISBN (Electronic)9783319461281
ISBN (Print)9783319461274
DOIs
Publication statusPublished - 2016
Externally publishedYes
Event15th European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2016 - Riva del Garda, Italy
Duration: 19 Sep 201623 Sep 2016

Publication series

NameLecture Notes in Computer Science
Volume9851
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other15th European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2016
CountryItaly
CityRiva del Garda
Period19/09/1623/09/16

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  • Cite this

    Wang, S., Nie, F., Chang, X., Li, X., Sheng, Q. Z., & Yao, L. (2016). Uncovering locally discriminative structure for feature analysis. In P. Frasconi, N. Landwehr, G. Manco, & J. Vreeken (Eds.), Machine learning and knowledge discovery in databases: European Conference, ECML PKDD 2016, Riva del Garda, Italy, September 19–23, 2016, proceedings, part I (pp. 281-295). (Lecture Notes in Computer Science; Vol. 9851). Cham: Springer, Springer Nature. https://doi.org/10.1007/978-3-319-46128-1_18