A scalable unsupervised feature merging approach to efficient dimensionality reduction of high-dimensional visual data
To achieve a good trade-off between recognition accuracy and computational efficiency, it is often needed to reduce high-dimensional visual data to medium-dimensional ones. For this task, even applying a simple full-matrix-based linear projection causes significant computation and memory use. When the number of visual data is large, how to efficiently learn such a projection could even become a problem. The recent feature merging approach offers an efficient way to reduce the dimensionality,...[Show more]
|Collections||ANU Research Publications|
|Source:||Proceedings of the IEEE International Conference on Computer Vision|
|01_Liu_A_scalable_unsupervised_2013.pdf||430.96 kB||Adobe PDF||Request a copy|
|02_Liu_A_scalable_unsupervised_2013.pdf||54.2 kB||Adobe PDF||Request a copy|
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