Semi-supervised distance metric learning based on local linear regression for data clustering
Distance metric plays an important role in many machine learning tasks. The distance between samples is mostly measured with a predefined metric, ignoring how the samples distribute in the feature space and how the features are correlated. This paper proposes a semi-supervised distance metric learni...
Main Authors: | Yu, Jun., Wang, Meng., Liu, Yun., Zhang, Hong. |
---|---|
Other Authors: | School of Electrical and Electronic Engineering |
Format: | Journal Article |
Language: | English |
Published: |
2013
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/85068 http://hdl.handle.net/10220/13661 |
Similar Items
-
Learning Bregman distance functions for semi-supervised clustering
by: Wu, Lei., et al.
Published: (2013) -
Semi-supervised hierarchical clustering for personalized web image organization
by: Meng, Lei, et al.
Published: (2013) -
Semi-supervised clustering algorithms for web documents
by: Hua, Yunke.
Published: (2013) -
Semi-supervised clustering algorithms for web documents
by: Bian, Zhiwei.
Published: (2011) -
Semi-supervised clustering techniques for categorization of text documents
by: Yan, Yang
Published: (2015)