Abstract:
We address the problem of feature weight learning for image clustering. In practice, before clustering data, we generally normalize all data features between 0 and 1, bec...Show MoreMetadata
Abstract:
We address the problem of feature weight learning for image clustering. In practice, before clustering data, we generally normalize all data features between 0 and 1, because we cannot determine which features are more important. In this paper, we provide a feature weight learning framework for clustering which can obtain the feature weights and cluster labels simultaneously. An alternative optimization algorithm is adopted to solve this problem. Empirical studies on the toy data and real image data demonstrate our algorithm's effectiveness in improving the clustering performance.
Date of Conference: 12-15 October 2008
Date Added to IEEE Xplore: 12 December 2008
ISBN Information: