Author | Conference | Journal | Organization | Year | DOI
Look for results that meet for the following criteria:
Academic
Publications
Unsupervised Sequential Forward Dimensionality Reduction Based on Fractal

Unsupervised Sequential Forward Dimensionality Reduction Based on Fractal,10.1109/FSKD.2008.235,Guanghui Yan,Lisong Liu,Linna Du,Xiaxia Yang,Zhicheng

Edit
Unsupervised Sequential Forward Dimensionality Reduction Based on Fractal  
BibTex | RIS | RefWorks Download
Dimensionality reduction has long been an active research topic within statistics, pattern recognition, machine learning and data mining. It can improve the efficiency and the effectiveness of data mining by reducing the dimensions of feature space and removing the irrelevant and redundant information. In this paper, we transform the attribute selection problem into the optimization problem which tries to find the attribute subset with the maximal fractal dimension and the attribute number restriction simultaneously. In order to avoid exhaustive search in the huge attribute subset space we integrate the individual attribute priority with attribute subset evaluation for dimensionality reduction and propose the unsupervised Sequential Forward Fractal Dimensionality Reduction(SFFDR) algorithm. Our experiments on synthetic and real datasets show that the algorithm proposed can get the satisfied resulting attribute subset with a rather low time complexity.
Conference: Fuzzy Systems and Knowledge Discovery , pp. 48-52, 2008
Cumulative Annual
View Publication
The following links allow you to view full publications. These links are maintained by other sources not affiliated with Microsoft Academic Search.
More »
Share this on