.png){kind=icon}
.png){kind=icon}
Return
Constrained Concept Factorization for Image Representation
.png){kind=icon}
PRE
AI
DOI：10.1109/TCYB.2013.2287103.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Matrix factorization based techniques, such as nonnegative matrix factorization and concept factorization, have attracted great attention in dimensionality reduction and data clustering. Previous studies show that both of them yield impressive results on image processing and document clustering. However, both of them are essentially unsupervised methods and cannot incorporate label information. In this paper, we propose a novel semisupervised matrix decomposition method for extracting the image concepts that are consistent with the known label information. With this constraint, we call the new approach constrained concept factorization. By requiring that the data points sharing the same label have the same coordinate in the new representation space, this approach has more discriminating power. The experimental results on several corpora show good performance of our novel algorithm in terms of clustering accuracy and mutual information.
Keywords:
Clustering
dimensionality reduction
nonnegative matrix factorization
semisupervised learning
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
10.5
Papers:
1.1W
Citations:
5.0W