Computational geometry: an introduction
Computational geometry: an introduction
Computing in Science and Engineering
Clustering by Scale-Space Filtering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift, Mode Seeking, and Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Improved Fast Gauss Transform and Efficient Kernel Density Estimation
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - 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
Integrating constraints and metric learning in semi-supervised clustering
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Exploratory Data Analysis with MATLAB (Computer Science and Data Analysis)
Exploratory Data Analysis with MATLAB (Computer Science and Data Analysis)
Active sampling for multiple output identification
Machine Learning
An efficient algorithm for local distance metric learning
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Multiresolution instance-based learning
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
RADAR: rare category detection via computation of boundary degree
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part II
Hierarchical blurring mean-shift
ACIVS'11 Proceedings of the 13th international conference on Advanced concepts for intelligent vision systems
A unifying theory of active discovery and learning
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part V
Proceedings of the 34th ACM SIGPLAN conference on Programming language design and implementation
Active Rare Class Discovery and Classification Using Dirichlet Processes
International Journal of Computer Vision
Expert Systems with Applications: An International Journal
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Many applications in surveillance, monitoring, scientific discovery, and data cleaning require the identification of anomalies. Although many methods have been developed to identify statistically significant anomalies, a more difficult task is to identify anomalies that are both interesting and statistically significant. Category detection is an emerging area of machine learning that can help address this issue using a "human-in-the-loop" approach. In this interactive setting, the algorithm asks the user to label a query data point under an existing category or declare the query data point to belong to a previously undiscovered category. The goal of category detection is to bring to the user's attention a representative data point from each category in the data in as few queries as possible. In a data set with imbalanced categories, the main challenge is in identifying the rare categories or anomalies; hence, the task is often referred to as rare category detection. We present a new approach to rare category detection based on hierarchical mean shift. In our approach, a hierarchy is created by repeatedly applying mean shift with an increasing bandwidth on the data. This hierarchy allows us to identify anomalies in the data set at different scales, which are then posed as queries to the user. The main advantage of this methodology over existing approaches is that it does not require any knowledge of the dataset properties such as the total number of categories or the prior probabilities of the categories. Results on real-world data sets show that our hierarchical mean shift approach performs consistently better than previous techniques.