Title: Rare Category Detection Using Hierarchical Mean Shift
1(Rare) Category Detection Using Hierarchical Mean
Shift
- Pavan Vatturi (vatturi_at_eecs.oregonstate.edu)
- Weng-Keen Wong (wong_at_eecs.oregonstate.edu)
21. Introduction
- Applications for surveillance, scientific
discovery and data cleaning require anomaly
detection - Anomalies often identified as statistically
unusual data points - Many detected anomalies are simply uninteresting
or correspond to known sources of noise
31. Introduction
Known objects (99.9 of the data)
Anomalies (0.1 of the data)
Pictures from Sloan Digital Sky Survey
(http//www.sdss.org/iotw/archive.html) Pelleg,
D. (2004). Scalable and Practical Probability
Density Estimators for Scientific Anomaly
Detection. PhD Thesis, Carnegie Mellon
University.
Uninteresting (99 of anomalies)
Interesting (1 of anomalies)
41. Introduction
- Category Detection Pelleg and Moore 2004
human-in-the-loop exploratory data analysis
Ask User to Label Categories of Interesting Data
Points
Data Set
Update Model with Labels
Build Model
Spot Interesting Data Points
51. Introduction
- User can
- Label a query data point under an existing
category - Or declare data point to belong to a previous
undeclared category
Ask User to Label Categories of Interesting Data
Points
Data Set
Update Model with Labels
Build Model
Spot Interesting Data Points
61. Introduction
- Goal present to user a single instance from each
category in as few queries as possible - Difficult to detect rare categories if class
imbalance is severe - Interested in rare categories for anomaly
detection
7Outline
- Introduction
- Related Work
- Background
- Methodology
- Results
- Conclusion / Future Work
82. Related Work
- Interleave Pelleg and Moore 2004
- Nearest-Neighbor-based active learning for
rare-category detection for multiple classes He
and Carbonell 2008 - Multiple output identification Fine and Mansour
2006
93. Background Mean Shift Fukunaga and Hostetler
1975
Reference data set
Mean shift vector (follows density gradient)
Query point
Center of Mass
Mean shift vector with kernel k
103. Background Mean Shift Fukunaga and Hostetler
1975
Reference data set
Convergence to cluster center
Query point
Center of Mass
113. Background Mean Shift Blurring
Reference data set
Query point
Center of Mass
- Blurring
- When query points are the same as the reference
data set - Progressively blurs the original data set
123. Background Mean Shift
End result of applying mean shift to a synthetic
data set
134. Methodology Overview
- Sphere the data
- Hierarchical Mean Shift
- Query user
144. Methodology Hierarchical Mean Shift
Repeatedly blur data using Mean Shift with
increasing bandwidth hnew k hold
154. Methodology Querying the User
- The data point closest to the cluster center is
the representative data point. Rank
representative data points for querying to user
according to - Outlierness Leung et al. 2000 for Cluster Ci
Lifetime of Ci Log (bandwidth when cluster Ci
is merged with other clusters bandwidth when
cluster Ci is formed)
164. Methodology Querying the User
- Rank representative data points for querying to
user according to - Compactness Isolation Leung et al. 2000 for
Cluster Ci
174. Methodology Tiebreaker
- Ties may occur in Outlierness or
Compactness/Isolation values. - Highest Average Distance heuristic choose
representative data point with highest average
distance from user-labeled points.
185. Results
Data sets used in experiments
Shuttle, OptDigits, OptLetters, and Statlog were
subsampled to simulate class imbalance.
195. Results (Yeast)
Category detection metric queries before user
presented with at least one example from all
categories
205. Results
Number of hints to discover all classes
215. Results
Area under the category detection curve
226. Conclusion / Future Work
- Conclusions
- HMS-based methods consistently discover more
categories in fewer queries than existing methods - Do not need apriori knowledge of dataset
properties
236. Conclusion / Future Work
- Future Work
- Better use of user feedback
- Presentation of an entire cluster to the user
instead of a representative data point - Improved computational efficiency
- Theoretical analysis