2D spiral pattern recognition with possibilistic measures
Pattern Recognition Letters
Locally Adaptive Metric Nearest-Neighbor Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
On Using Partial Supervision for Text Categorization
IEEE Transactions on Knowledge and Data Engineering
Fast k-Nearest Neighbor Classification Using Cluster-Based Trees
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptive Quasiconformal Kernel Nearest Neighbor Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
A New Classification Rule based on Nearest Neighbour Search
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
Information Fusion Methods Based on Physical Laws
IEEE Transactions on Pattern Analysis and Machine Intelligence
Nearest Neighbors by Neighborhood Counting
IEEE Transactions on Pattern Analysis and Machine Intelligence
A local mean-based nonparametric classifier
Pattern Recognition Letters
Improving nearest neighbor classification with cam weighted distance
Pattern Recognition
A shrinking-based approach for multi-dimensional data analysis
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Letters: Adaptive local hyperplane classification
Neurocomputing
Local relative transformation with application to isometric embedding
Pattern Recognition Letters
Data gravitation based classification
Information Sciences: an International Journal
Information-Theoretic Distance Measures for Clustering Validation: Generalization and Normalization
IEEE Transactions on Knowledge and Data Engineering
Using graph algebra to optimize neighborhood for isometric mapping
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Tailored Aggregation for Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
An adaptable k-nearest neighbors algorithm for MMSE image interpolation
IEEE Transactions on Image Processing
General Geometric Good Continuation: From Taylor to Laplace via Level Sets
International Journal of Computer Vision
Adaptive local hyperplane for regression tasks
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Extraction of building polygons from SAR images: Grouping and decision-level in the GESTALT system
Pattern Recognition Letters
Learning from examples in the small sample case: face expression recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
The Nearest Neighbor Algorithm of Local Probability Centers
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Nearest neighbor pattern classification
IEEE Transactions on Information Theory
LDA/SVM driven nearest neighbor classification
IEEE Transactions on Neural Networks
Face Recognition Using Total Margin-Based Adaptive Fuzzy Support Vector Machines
IEEE Transactions on Neural Networks
Perceptual relativity-based semi-supervised dimensionality reduction algorithm
Applied Soft Computing
| Hi-index | 0.01 |
When performing the classification on the high dimensional, the sparse, or the noisy data, many approaches easily lead to the dramatic performance degradation. To deal with this issue from the different perspective, this paper proposes a cognitive gravitation model (CGM) based on both the law of gravitation in physics and the cognitive laws, where the self-information of each sample instead of mass is applied. Subsequently, a new classifier is designed which utilizes CGM to find k nearest neighbors from each class for the query sample and then classifies this query sample to the class whose cognitive gravitation is largest. The cognitive gravitation of the class is defined as the sum of the cognitive gravitation between its each nearest neighbor and the query sample. The advantage of our approach is that it has a firm and simple mathematical basis while it has good classification performance. The conducted experiments on challenging benchmark data sets validate the proposed model and the classification approach.