NDLI: Semi-supervised Node Splitting for Random Forest Construction

Content Provider	IEEE Xplore Digital Library
Author	Xiao Liu Mingli Song Dacheng Tao Zicheng Liu Luming Zhang Chun Chen Jiajun Bu
Copyright Year	2013
Description	Author affiliation: Centre for Quantum Comput. & Intell. Syst., Univ. of Technol. Sydney, Sydney, NSW, Australia (Dacheng Tao) \|\| Coll. of Comput. Sci., Zhejiang Univ., Hangzhou, China (Xiao Liu; Mingli Song; Luming Zhang; Chun Chen; Jiajun Bu) \|\| Microsoft Res., Redmond, WA, USA (Zicheng Liu)
Abstract	Node splitting is an important issue in Random Forest but robust splitting requires a large number of training samples. Existing solutions fail to properly partition the feature space if there are insufficient training data. In this paper, we present semi-supervised splitting to overcome this limitation by splitting nodes with the guidance of both labeled and unlabeled data. In particular, we derive a nonparametric algorithm to obtain an accurate quality measure of splitting by incorporating abundant unlabeled data. To avoid the curse of dimensionality, we project the data points from the original high-dimensional feature space onto a low-dimensional subspace before estimation. A unified optimization framework is proposed to select a coupled pair of subspace and separating hyper plane such that the smoothness of the subspace and the quality of the splitting are guaranteed simultaneously. The proposed algorithm is compared with state-of-the-art supervised and semi-supervised algorithms for typical computer vision applications such as object categorization and image segmentation. Experimental results on publicly available datasets demonstrate the superiority of our method.
Starting Page	492
Ending Page	499
File Size	465307
Page Count	8
File Format	PDF
ISBN	9780769549897
ISSN	10636919
DOI	10.1109/CVPR.2013.70
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-06-23
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Radio frequency Estimation Training Vegetation Training data Bandwidth Optimization random forest semi-supervised learning node splitting
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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