Sciweavers

IJCAI
2007
13 years 5 months ago
Improving Embeddings by Flexible Exploitation of Side Information
Dimensionality reduction is a much-studied task in machine learning in which high-dimensional data is mapped, possibly via a non-linear transformation, onto a low-dimensional mani...
Ali Ghodsi, Dana F. Wilkinson, Finnegan Southey
MICCAI
2009
Springer
13 years 10 months ago
On the Manifold Structure of the Space of Brain Images
This paper investigates an approach to model the space of brain images through a low-dimensional manifold. A data driven method to learn a manifold from a collections of brain imag...
Samuel Gerber, Tolga Tasdizen, Sarang C. Joshi, Ro...
CVPR
2010
IEEE
13 years 11 months ago
Manifold Blurring Mean Shift Algorithms
We propose a new family of algorithms for denoising data assumed to lie on a low-dimensional manifold. The algorithms are based on the blurring mean-shift update, which moves each...
Weiran Wang, Miguel Carreira-perpinan
CVPR
2007
IEEE
14 years 5 months ago
Adaptive Distance Metric Learning for Clustering
A good distance metric is crucial for unsupervised learning from high-dimensional data. To learn a metric without any constraint or class label information, most unsupervised metr...
Jieping Ye, Zheng Zhao, Huan Liu