Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
Augmenting Naive Bayes Classifiers with Statistical Language Models
Information Retrieval
An empirical study of required dimensionality for large-scale latent semantic indexing applications
Proceedings of the 17th ACM conference on Information and knowledge management
Fuzzy SVM for noisy data: a robust membership calculation method
FUZZ-IEEE'09 Proceedings of the 18th international conference on Fuzzy Systems
Accurate estimation of ICA weight matrix by implicit constraint imposition using lie group
IEEE Transactions on Neural Networks
Beyond classical consensus clustering: The least squares approach to multiple solutions
Pattern Recognition Letters
Model order selection for boolean matrix factorization
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Session-based classification of internet applications in 3G wireless networks
Computer Networks: The International Journal of Computer and Telecommunications Networking
Fusion and inference from multiple data sources in a commensurate space
Statistical Analysis and Data Mining
| Hi-index | 0.03 |
Most dimension reduction techniques produce ordered coordinates so that only the first few coordinates need be considered in subsequent analyses. The choice of how many coordinates to use is often made with a visual heuristic, i.e., by making a scree plot and looking for a ''big gap'' or an ''elbow.'' In this article, we present a simple and automatic procedure to accomplish this goal by maximizing a simple profile likelihood function. We give a wide variety of both simulated and real examples.