Label Propagation through Linear Neighborhoods

In many practical data mining applications such as text classification, unlabeled training examples are readily available, but labeled ones are fairly expensive to obtain. Therefore, semi supervised learning algorithms have aroused considerable interests from the data mining and machine learning fie...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	IEEE transactions on knowledge and data engineering Ročník 20; číslo 1; s. 55 - 67
Hlavní autoři:	Wang, Fei, Zhang, Changshui
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York, NY IEEE 01.01.2008 IEEE Computer Society The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Algorithms Applied sciences Artificial intelligence Classification Computer science; control theory; systems Data mining Data processing. List processing. Character string processing Exact sciences and technology Gaussian processes Graph labeling Labels Learning Machine learning Machine learning algorithms Memory organisation. Data processing Mining methods and algorithms Predictive models Semifabricated products Semisupervised learning Software Speech and sound recognition and synthesis. Linguistics Studies Supervised learning Support vector machine classification Support vector machines Tasks Text categorization Texts Unsupervised learning Data mining Graph labeling Mining methods and algorithms Machine learning Data analysis semisupervised learning label propagation Information retrieval Information extraction Text Linear model graph Structured data mining Natural language Data structure linear neighborhoods Learning algorithm Artificial intelligence Content analysis
ISSN:	1041-4347, 1558-2191
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Popis
Shrnutí:	In many practical data mining applications such as text classification, unlabeled training examples are readily available, but labeled ones are fairly expensive to obtain. Therefore, semi supervised learning algorithms have aroused considerable interests from the data mining and machine learning fields. In recent years, graph-based semi supervised learning has been becoming one of the most active research areas in the semi supervised learning community. In this paper, a novel graph-based semi supervised learning approach is proposed based on a linear neighborhood model, which assumes that each data point can be linearly reconstructed from its neighborhood. Our algorithm, named linear neighborhood propagation (LNP), can propagate the labels from the labeled points to the whole data set using these linear neighborhoods with sufficient smoothness. A theoretical analysis of the properties of LNP is presented in this paper. Furthermore, we also derive an easy way to extend LNP to out-of-sample data. Promising experimental results are presented for synthetic data, digit, and text classification tasks.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 ObjectType-Article-2 ObjectType-Feature-1 content type line 23
ISSN:	1041-4347 1558-2191
DOI:	10.1109/TKDE.2007.190672