Applications of spatial data structures: Computer graphics, image processing, and GIS
Applications of spatial data structures: Computer graphics, image processing, and GIS
Vector quantization and signal compression
Vector quantization and signal compression
Weighted Parzen Windows for Pattern Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Very fast EM-based mixture model clustering using multiresolution kd-trees
Proceedings of the 1998 conference on Advances in neural information processing systems II
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast Parzen Density Estimation Using Clustering-Based Branch and Bound
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Efficient k-Means Clustering Algorithm: Analysis and Implementation
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Algorithm for Data-Driven Bandwidth Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Similarity Search in High Dimensions via Hashing
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift Analysis and Applications
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Mean Shift Based Clustering in High Dimensions: A Texture Classification Example
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Video retrieval using spatio-temporal descriptors
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Information Fusion Framework for Robust Shape Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Applying Neighborhood Consistency for Fast Clustering and Kernel Density Estimation
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Clustering with Bregman Divergences
The Journal of Machine Learning Research
Simplifying Mixture Models Using the Unscented Transform
IEEE Transactions on Pattern Analysis and Machine Intelligence
Density-weighted nyström method for computing large kernel eigensystems
Neural Computation
Nonparametric belief propagation
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Probability density estimation from optimally condensed data samples
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast Multiclass SVM Classification Using Decision Tree Based One-Against-All Method
Neural Processing Letters
Improving feature space based image segmentation via density modification
Information Sciences: an International Journal
Planning for multiple measurement channels in a continuous-state POMDP
Annals of Mathematics and Artificial Intelligence
Incremental object learning and robust tracking of multiple objects from RGB-D point set data
Journal of Visual Communication and Image Representation
Hi-index | 0.00 |
The finite mixture model is widely used in various statistical learning problems. However, the model obtained may contain a large number of components, making it inefficient in practical applications. In this paper, we propose to simplify the mixture model by minimizing an upper bound of the approximation error between the original and the simplified model, under the use of the L2 distance measure. This is achieved by first grouping similar components together and then performing local fitting through function approximation. The simplified model obtained can then be used as a replacement of the original model to speed up various algorithms involving mixture models during training (e.g., Bayesian filtering, belief propagation) and testing [e.g., kernel density estimation, support vector machine (SVM) testing]. Encouraging results are observed in the experiments on density estimation, clustering-based image segmentation, and simplification of SVM decision functions.