18045673. AUTOMATED SPARSITY FEATURE SELECTION simplified abstract (INTERNATIONAL BUSINESS MACHINES CORPORATION)
Contents
- 1 AUTOMATED SPARSITY FEATURE SELECTION
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 AUTOMATED SPARSITY FEATURE SELECTION - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 How does this technology compare to traditional anomaly detection methods?
- 1.11 What are the computational requirements for implementing this technology?
- 1.12 Original Abstract Submitted
AUTOMATED SPARSITY FEATURE SELECTION
Organization Name
INTERNATIONAL BUSINESS MACHINES CORPORATION
Inventor(s)
Girmaw Abebe Tadesse of Nairobi (KE)
William Ogallo of Nairobi (KE)
George Sirbu of Saline MI (US)
Skyler Speakman of Nairobi (KE)
AUTOMATED SPARSITY FEATURE SELECTION - A simplified explanation of the abstract
This abstract first appeared for US patent application 18045673 titled 'AUTOMATED SPARSITY FEATURE SELECTION
Simplified Explanation
The patent application describes a method where computer processors identify an anomalous subset using sparsity-based automatic feature selection.
- The innovation involves utilizing computer processors to automatically select features based on sparsity, which helps in identifying anomalous subsets efficiently.
- By focusing on sparse features, the processors can pinpoint unusual patterns or outliers within a dataset, leading to the discovery of anomalous subsets.
- This method streamlines the process of anomaly detection by leveraging sparsity-based feature selection, enhancing the accuracy and speed of identifying outliers.
Potential Applications
The technology can be applied in various fields such as:
- Fraud detection in financial transactions
- Intrusion detection in cybersecurity
- Quality control in manufacturing processes
Problems Solved
This technology addresses the following issues:
- Time-consuming manual feature selection processes
- Difficulty in identifying anomalous subsets within large datasets
- Inefficient anomaly detection methods
Benefits
The benefits of this technology include:
- Improved accuracy in detecting anomalies
- Faster identification of outliers
- Enhanced efficiency in anomaly detection processes
Potential Commercial Applications
The technology can be utilized in industries such as:
- Finance and banking
- Cybersecurity firms
- Manufacturing companies
Possible Prior Art
One possible prior art could be the use of machine learning algorithms for anomaly detection, but the specific focus on sparsity-based automatic feature selection may be a novel aspect of this innovation.
Unanswered Questions
How does this technology compare to traditional anomaly detection methods?
This article does not provide a direct comparison between this technology and traditional anomaly detection methods. It would be beneficial to understand the specific advantages and limitations of this innovation in comparison to existing approaches.
What are the computational requirements for implementing this technology?
The article does not delve into the computational resources needed to deploy this technology. Understanding the hardware and software requirements could be crucial for organizations considering adopting this innovation.
Original Abstract Submitted
One or more computer processors discover an anomalous subset through sparsity-based automatic feature selection.
