Multidimensional Embedding Spaces for Data Classification
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Solution Overview
Problem
Existing data management systems face challenges in efficiently classifying, correlating, and profiling data objects due to their inability to effectively map data objects to embedding spaces, leading to reduced usability and lost economies of scale.
Innovation Solution
The creation and utilization of customized multidimensional embedding spaces for data objects, which includes identifying key characteristics and generating object vectors for input to machine learning algorithms, facilitating accurate classification and correlation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data objects are mapped to embedding spaces using traditional methods, then the mapping process is simple, but the classification accuracy and relationship discovery capability are reduced
Solution Approach 1:
The patent transitions from traditional flat data structures to multidimensional embedding spaces where data objects are represented as vectors in n-dimensional space. This dimensional transformation enables machine learning algorithms to capture complex relationships and patterns that are not apparent in traditional tabular formats, thereby improving classification accuracy while the system learns optimal dimensionality.
Solution Approach 2:
The patent dynamically adjusts embedding space parameters including dimensionality, vector normalization, and distance metrics based on the specific data characteristics and classification task requirements. This parameter optimization allows the system to achieve high accuracy by tailoring the embedding space configuration to each specific use case rather than using fixed traditional methods.
2Adaptability or versatility
If customized multidimensional embedding spaces are created for data objects, then relationship discovery and data utilization are enhanced, but the computational complexity and processing time increase
Solution Approach 1:
The patent pre-computes and stores embedding vectors for data objects in advance, transforming raw data into the multidimensional embedding space before actual classification or analysis tasks. This preliminary transformation allows subsequent queries and operations to work efficiently with pre-processed vectors, significantly reducing real-time processing time while maintaining high adaptability.
Solution Approach 2:
The patent creates vector representations (copies) of data objects in the embedding space that capture the essential characteristics and relationships. These vector copies enable efficient similarity searches, clustering, and classification operations without repeatedly processing the original complex data structures, thus reducing processing time while preserving data utility.
3Loss of information
If traditional data management systems are used, then the system structure is simple, but the ability to identify unknown relationships and correlations is limited
Solution Approach 1:
By representing data objects as vectors in high-dimensional embedding spaces, the patent enables machine learning algorithms to discover hidden patterns, similarities, and relationships that are not apparent in traditional data formats. The dimensional representation preserves and amplifies subtle relationships, allowing the system to identify unknown correlations while the underlying complexity is managed through established ML frameworks.
Data Source
AI summary
Various embodiments are generally directed to techniques for creating and utilizing multidimensional embedding spaces for data objects, such as to condition the data for input to a neural network, for instance. Some embodiments are particularly directed to converting semi-structured data, such as a set of data objects, into object vector sets mapped to a multidimensional embedding space. In many embodiments, an embedding space for a set of data objects may be customized with a set of dimensions that correspond to various characteristics of the set of data objects. These and other embodiments are described and claimed.