TY - GEN
T1 - Transfer spectral clustering
AU - Jiang, Wenhao
AU - Chung, Fu Lai Korris
PY - 2012/10/4
Y1 - 2012/10/4
N2 - Transferring knowledge from auxiliary datasets has been proved useful in machine learning tasks. Its adoption in clustering however is still limited. Despite of its superior performance, spectral clustering has not yet been incorporated with knowledge transfer or transfer learning. In this paper, we make such an attempt and propose a new algorithm called transfer spectral clustering (TSC). It involves not only the data manifold information of the clustering task but also the feature manifold information shared between related clustering tasks. Furthermore, it makes use of co-clustering to achieve and control the knowledge transfer among tasks. As demonstrated by the experimental results, TSC can greatly improve the clustering performance by effectively using auxiliary unlabeled data when compared with other state-of-the-art clustering algorithms.
AB - Transferring knowledge from auxiliary datasets has been proved useful in machine learning tasks. Its adoption in clustering however is still limited. Despite of its superior performance, spectral clustering has not yet been incorporated with knowledge transfer or transfer learning. In this paper, we make such an attempt and propose a new algorithm called transfer spectral clustering (TSC). It involves not only the data manifold information of the clustering task but also the feature manifold information shared between related clustering tasks. Furthermore, it makes use of co-clustering to achieve and control the knowledge transfer among tasks. As demonstrated by the experimental results, TSC can greatly improve the clustering performance by effectively using auxiliary unlabeled data when compared with other state-of-the-art clustering algorithms.
KW - Co-clustering
KW - Spectral Clustering
KW - Transfer Learning
UR - http://www.scopus.com/inward/record.url?scp=84866848918&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-33486-3_50
DO - 10.1007/978-3-642-33486-3_50
M3 - Conference article published in proceeding or book
SN - 9783642334856
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 789
EP - 803
BT - Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2012, Proceedings
T2 - 2012 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML-PKDD 2012
Y2 - 24 September 2012 through 28 September 2012
ER -