The nature of statistical learning theory
The nature of statistical learning theory
Learning to Recognize Volcanoes on Venus
Machine Learning - Special issue on applications of machine learning and the knowledge discovery process
Exploiting generative models in discriminative classifiers
Proceedings of the 1998 conference on Advances in neural information processing systems II
Concept decompositions for large sparse text data using clustering
Machine Learning
Unsupervised Feature Selection Using Feature Similarity
IEEE Transactions on Pattern Analysis and Machine Intelligence
Finding overlapping components with MML
Statistics and Computing
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Simultaneous Feature Selection and Clustering Using Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical and Inductive Inference by Minimum Message Length (Information Science and Statistics)
Statistical and Inductive Inference by Minimum Message Length (Information Science and Statistics)
On-line EM Algorithm for the Normalized Gaussian Network
Neural Computation
Online clustering of parallel data streams
Data & Knowledge Engineering
Machine-learning paradigms for selecting ecologically significant input variables
Engineering Applications of Artificial Intelligence
Detecting image spam using visual features and near duplicate detection
Proceedings of the 17th international conference on World Wide Web
Review: A review of machine learning approaches to Spam filtering
Expert Systems with Applications: An International Journal
A Hybrid Feature Extraction Selection Approach for High-Dimensional Non-Gaussian Data Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
On multivariate binary data clustering and feature weighting
Computational Statistics & Data Analysis
IEEE Transactions on Knowledge and Data Engineering
Improved Online Support Vector Machines Spam Filtering Using String Kernels
CIARP '09 Proceedings of the 14th Iberoamerican Conference on Pattern Recognition: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Language-model-based detection cascade for efficient classification of image-based spam e-mail
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Efficient Modeling of Spam Images
IITSI '10 Proceedings of the 2010 Third International Symposium on Intelligent Information Technology and Security Informatics
A study of spam filtering using support vector machines
Artificial Intelligence Review
Unsupervised Feature Selection and Learning for Image Segmentation
CRV '10 Proceedings of the 2010 Canadian Conference on Computer and Robot Vision
A mixture model-based on-line CEM algorithm
IDA'05 Proceedings of the 6th international conference on Advances in Intelligent Data Analysis
IEEE Transactions on Signal Processing
Paper: Modeling by shortest data description
Automatica (Journal of IFAC)
IEEE Transactions on Neural Networks
Mixtures of von Mises Distributions for People Trajectory Shape Analysis
IEEE Transactions on Circuits and Systems for Video Technology
Spatial distance join based feature selection
Engineering Applications of Artificial Intelligence
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Motivated by the high demand to construct compact and accurate statistical models that are automatically adjustable to dynamic changes, in this paper, we propose an online probabilistic framework for high-dimensional spherical data modeling. The proposed framework allows simultaneous clustering and feature selection in online settings using finite mixtures of von Mises distributions (movM). The unsupervised learning of the resulting model is approached using Expectation Maximization (EM) for parameter estimation along with minimum message length (MML) to determine the optimal number of mixture components. The gradient stochastic descent approach is considered for incremental updating of model parameters, also. Through empirical experiments, we demonstrate the merits of the proposed learning framework on diverse high dimensional datasets and challenging applications.