TY - JOUR
T1 - Semi-supervised sub-manifold discriminant analysis
AU - Song, Yangqiu
AU - Nie, Feiping
AU - Zhang, Changshui
PY - 2008/10/1
Y1 - 2008/10/1
N2 - In this paper, we present a semi-supervised sub-manifold discriminant analysis algorithm. To separate each sub-manifold constructed by each class, we define the within-manifold scatter, between-manifold scatter and total-manifold scatter matrices. The scatter matrices are robust to outlier and diverse-density clusters. Kernelization and direct non-linear embedding are also developed. Experimental results show that our approach can give competitive results in comparison to the state-of-the-art algorithms.
AB - In this paper, we present a semi-supervised sub-manifold discriminant analysis algorithm. To separate each sub-manifold constructed by each class, we define the within-manifold scatter, between-manifold scatter and total-manifold scatter matrices. The scatter matrices are robust to outlier and diverse-density clusters. Kernelization and direct non-linear embedding are also developed. Experimental results show that our approach can give competitive results in comparison to the state-of-the-art algorithms.
KW - Dimensionality reduction
KW - Semi-supervised learning
KW - Sub-manifold discriminative embedding
UR - http://www.scopus.com/inward/record.url?scp=48649090541&partnerID=8YFLogxK
U2 - 10.1016/j.patrec.2008.05.024
DO - 10.1016/j.patrec.2008.05.024
M3 - 文章
AN - SCOPUS:48649090541
SN - 0167-8655
VL - 29
SP - 1806
EP - 1813
JO - Pattern Recognition Letters
JF - Pattern Recognition Letters
IS - 13
ER -