Algorithms for clustering data
Algorithms for clustering data
Probabilistic Visual Learning for Object Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
GTM: the generative topographic mapping
Neural Computation
A Unified Model for Probabilistic Principal Surfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Minimum-Entropy Data Partitioning Using Reversible Jump Markov Chain Monte Carlo
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Learning of Finite Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Accelerating EM for Large Databases
Machine Learning
ICMI '02 Proceedings of the 4th IEEE International Conference on Multimodal Interfaces
Statistical Background Subtraction for a Mobile Observer
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Simultaneous Feature Selection and Clustering Using Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
SMEM Algorithm for Mixture Models
Neural Computation
Time Series Classification Using Gaussian Mixture Models of Reconstructed Phase Spaces
IEEE Transactions on Knowledge and Data Engineering
Man-made structure detection in natural images using a causal multiscale random field
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Image segmentation in video sequences: a probabilistic approach
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Modeling the manifolds of images of handwritten digits
IEEE Transactions on Neural Networks
Ellipsoidal decision regions for motif-based patterned fabric defect detection
Pattern Recognition
Pattern Recognition
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Gaussian Mixture Models (GMM) have been broadly applied for the fitting of probability density function. However, due to the intrinsic linearity of GMM, usually many components are needed to appropriately fit the data distribution, when there are curve manifolds in the data cloud. In order to solve this problem and represent data with curve manifolds better, in this paper we propose a new nonlinear probability model, called active curve axis Gaussian model. Intuitively, this model can be imagined as Gaussian model being bent at the first principal axis. For estimating parameters of mixtures of this model, the EM algorithm is employed. Experiments on synthetic data and Chinese characters show that the proposed nonlinear mixture models can approximate distributions of data clouds with curve manifolds in a more concise and compact way than GMM does. The performance of the proposed nonlinear mixture models is promising.