Distance-Based Vector Classification for Anomaly Detection
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Solution Overview
Problem
Anomaly detection algorithms face challenges in high-dimensional spaces due to the curse of dimensionality, leading to sparse data and computationally intractable analysis, and they often require significant computational resources.
Innovation Solution
The method employs distance-based vector classification that transforms data from high-dimensional to low-dimensional spaces, using clustering algorithms like k-means to retain meaningful properties and reduce computational power, while adapting to unlabeled anomaly samples.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If anomaly detection algorithms operate in high-dimensional spaces, then they can capture more features and patterns, but data becomes sparse and computationally intractable analysis occurs
Solution Approach 1:
The patent transforms data from high-dimensional space to low-dimensional space using dimensionality reduction techniques. This resolves the contradiction by changing the dimensional representation of data, allowing anomaly detection to maintain accuracy while reducing computational complexity and avoiding the curse of dimensionality.
Solution Approach 2:
The patent extracts and retains only the meaningful properties and essential features from high-dimensional data during the dimensionality reduction process. This allows the system to eliminate redundant dimensions that contribute to sparsity and computational intractability while preserving the critical information needed for accurate anomaly detection.
2Reliability
If conventional convolutional learning-based techniques are used for anomaly detection, then detection capability is improved, but significant computational resources are required
Solution Approach 1:
The patent employs lightweight dimensionality reduction techniques that require significantly fewer computational resources compared to conventional convolutional learning-based methods. This approach uses simpler mathematical operations that consume less energy and computational power while still achieving effective anomaly detection, making the system more efficient and scalable.
Solution Approach 2:
By transforming the problem from high-dimensional convolutional processing to low-dimensional vector space operations, the patent dramatically reduces the computational burden. This dimensional transformation allows anomaly detection to be performed with much lower computational resources while maintaining detection effectiveness.
3Productivity
If data is transformed from high-dimensional to low-dimensional spaces, then computational efficiency is improved, but meaningful properties must be retained
Solution Approach 1:
The patent carefully extracts and preserves the meaningful properties and essential characteristics of the data during dimensionality reduction. By identifying and retaining only the most relevant features and patterns, the system ensures that critical information is not lost while achieving computational efficiency in the reduced-dimensional space.
Solution Approach 2:
The patent transforms data representation by changing the parameters and dimensions used to describe the data. This parameter transformation allows the system to maintain the essential meaningful properties of the original high-dimensional data while representing it in a more computationally efficient low-dimensional format suitable for anomaly detection.
Data Source
AI summary
Aspects of the present invention disclose a method for a distance-based vector classification in anomaly detection. The method includes one or more processors identifying one or more audio communications from a first user to a second user, wherein the one or more audio communications is transmitted utilizing a first computing device. The method further includes determining an objective of the first user based at least in part on the audio communication of the first user. The method further includes determining a set of conditions corresponding to the one or more audio communications and the objective, wherein the set of conditions indicate a vulnerability of personal data of the first user. The method further includes prohibiting the first computing device from transmitting audio data that includes the personal data of the first user.