A Kernel Approach for Vector Quantization with Guaranteed Distortion Bounds

By Michael E. Tipping, Bernhard Schölkopf. 2001

35 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Tobias Weyand
2. Yannis Kalantidis
3. Giorgos Tolias
4. Yannis Avrithis
5. Bastian Leibe
6. Yi Yang
7. Liang Zheng
8. Evaggelos Spyrou

Citations in the corpus

• Learning with Kernels: support vector machines, regularization, optimization, and beyond (2001, unknown): “, vM with minimal M such that (∗) holds (Tipping & Schölkopf, 2001 [66]) B.”
• Retrieving landmark and non-landmark images from community photo collections (2010, methodology): “We use the kernel vector quantization (KVQ) approach of Tipping and Schölkopf [34], along with an appropriate metric for each of the two cases above.”
• Visual landmark recognition from Internet photo collections: A large-scale evaluation (2014, methodology): “Kernel Vector Quantization (KVQ) [51] is a clustering method that selects a minimum number of points such that each point in the dataset is within radius r of at least one selected point.”