We have located links that may give you full text access.
Learning the Distribution Preserving Semantic Subspace for Clustering.
IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society 2017 September 5
This work proposes a new clustering method for images called Distribution Preserving Indexing (DPI). It aims to find a lower-dimensional semantic space approximating the original image space in the sense of preserving the distribution of the data. In the theory, the intrinsic structure of the data clusters can be described by the distribution of the data effectively. Therefore, the cluster structure of the data in a lower-dimensional semantic space derived by DPI becomes clear. Unlike these distance based clustering methods which reveal the intrinsic Euclidean structure of data, our method attempts to discover the intrinsic cluster structure of the data space that actually is the union of some sub-manifolds. Moreover, we propose a revised Kernel Density Estimator for the case of high-dimensional data, which is a crucial step in DPI. Also, we provide a theoretical analysis of the bound of our method. Finally, the extensive experiments compared with other algorithms, on COIL20, CBCL, and MNIST demonstrate the effectiveness of our proposed approach.
Full text links
Related Resources
Get seemless 1-tap access through your institution/university
For the best experience, use the Read mobile app
All material on this website is protected by copyright, Copyright © 1994-2024 by WebMD LLC.
This website also contains material copyrighted by 3rd parties.
By using this service, you agree to our terms of use and privacy policy.
Your Privacy Choices
You can now claim free CME credits for this literature searchClaim now
Get seemless 1-tap access through your institution/university
For the best experience, use the Read mobile app