TUTCRIS - Tampereen teknillinen yliopisto

TUTCRIS

Feature Dimensionality Reduction with Graph Embedding and Generalized Hamming Distance

Tutkimustuotosvertaisarvioitu

Yksityiskohdat

AlkuperäiskieliEnglanti
Otsikko2018 25th IEEE International Conference on Image Processing (ICIP)
KustantajaIEEE
Sivut1083-1087
Sivumäärä5
ISBN (elektroninen)978-1-4799-7061-2
ISBN (painettu)978-1-4799-7062-9
DOI - pysyväislinkit
TilaJulkaistu - lokakuuta 2018
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaIEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING -
Kesto: 1 tammikuuta 1900 → …

Julkaisusarja

Nimi
ISSN (elektroninen)2381-8549

Conference

ConferenceIEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING
Ajanjakso1/01/00 → …

Tiivistelmä

Principal component analysis (PCA) and linear discriminant analysis (LDA) are the most well-known methods to reduce the dimensionality of feature vectors. However, both methods face challenges when used on multilabel data - each data point may be associated to multiple labels. PCA does not take advantage of label information thus the performance is sacrificed. LDA can exploit class information for multiclass data, but cannot be directly applied to multilabel problems. In this paper, we propose a novel dimensionality reduction method for multilabel data. We first introduce the generalized Hamming distance that measures the distance of two data points in the label space. Then the proposed distance is used in the graph embedding framework for feature dimension reduction. We verified the proposed method using three multilabel benchmark datasets and one large image dataset. The results show that the proposed feature dimensionality reduction method consistently outperforms PCA and other competing methods.

Tutkimusalat

Julkaisufoorumi-taso