Multi-view based unlabeled data selection using feature transformation methods for semiboost learning
نویسندگان
چکیده
منابع مشابه
Multi-view based unlabeled data selection using feature transformation methods for semiboost learning
SemiBoost [23] is a boosting framework for semi-supervised learning, in which unlabeled data as well as labeled data both contribute to learning. Various strategies have been proposed in the literature to perform the task of selecting useful unlabeled data in SemiBoost. Recently, a multi-view based strategy was proposed in [20], in which the feature set of the data is decomposed into subsets (i...
متن کاملCombination of Feature Selection and Learning Methods for IoT Data Fusion
In this paper, we propose five data fusion schemes for the Internet of Things (IoT) scenario,which are Relief and Perceptron (Re-P), Relief and Genetic Algorithm Particle Swarm Optimization (Re-GAPSO), Genetic Algorithm and Artificial Neural Network (GA-ANN), Rough and Perceptron (Ro-P)and Rough and GAPSO (Ro-GAPSO). All the schemes consist of four stages, including preprocessingthe data set ba...
متن کاملUnified subspace learning for incomplete and unlabeled multi-view data
Multi-view data with each view corresponding to a type of feature set are common in real world. Usually, previous multi-view learning methods assume complete views. However, multi-view data are often incomplete, namely some samples have incomplete feature sets. Besides, most data are unlabeled due to a large cost of manual annotation, which makes learning of such data a challenging problem. In ...
متن کاملMulti-view Positive and Unlabeled Learning
Learning with Positive and Unlabeled instances (PU learning) arises widely in information retrieval applications. To address the unavailability issue of negative instances, most existing PU learning approaches require to either identify a reliable set of negative instances from the unlabeled data or estimate probability densities as an intermediate step. However, inaccurate negative-instance id...
متن کاملUnsupervised Feature Selection for Multi-View Data in Social Media
The explosive popularity of social media produces mountains of high-dimensional data and the nature of social media also determines that its data is often unlabelled, noisy and partial, presenting new challenges to feature selection. Social media data can be represented by heterogeneous feature spaces in the form of multiple views. In general, multiple views can be complementary and, when used ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Neurocomputing
سال: 2017
ISSN: 0925-2312
DOI: 10.1016/j.neucom.2017.04.021