Domain-Specific Feature Space Extension for Precise ML Representation
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Solution Overview
Problem
Existing machine learning techniques struggle with representing data samples from diverse domains using a shared feature space, which hinders the capture of domain-specific knowledge and leads to suboptimal distinction and representation of user behaviors and preferences.
Innovation Solution
The method involves obtaining a global representation for a feature and a local representation based on a classifying criterion, generating a combined representation that includes both, thereby creating an extended feature space tailored to specific domains.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a shared feature space is employed to represent data samples from all domains, then the system complexity is reduced, but the ability to capture domain-specific knowledge deteriorates
Solution Approach 1:
The patent divides the feature space into multiple domain-specific feature spaces, each dedicated to representing data from a particular domain. This segmentation allows each feature space to capture domain-specific characteristics without the interference of other domains, thereby preserving domain-specific knowledge while maintaining manageable system complexity through modular organization.
Solution Approach 2:
The patent extends the traditional single feature space by adding a domain dimension, creating a multi-dimensional feature space structure where data can be represented in domain-specific subspaces. This dimensional extension enables the system to maintain both global overview capabilities and domain-specific detail representation simultaneously.
2Ease of manufacture
If a shared feature space is used for all domains, then the implementation cost is reduced, but the representation precision of user behaviors and preferences deteriorates
Solution Approach 1:
The patent segments the feature representation system into multiple domain-specific feature spaces, allowing each domain to have its own optimized representation. This segmentation improves representation precision by capturing domain-specific patterns that would be diluted in a shared space, while the modular architecture keeps implementation costs manageable through reusable components.
Solution Approach 2:
The patent applies local quality by allowing different feature spaces to have different characteristics and optimization criteria tailored to their specific domains. Each domain-specific feature space can be optimized for its particular data characteristics, thereby improving representation precision locally without requiring complete redesign of the entire system.
3Loss of information
If domain-specific feature spaces are created for each domain, then the capture of domain-specific knowledge is improved, but the system complexity increases
Solution Approach 1:
The patent divides the overall feature space into multiple domain-specific feature spaces, each optimized for capturing knowledge from a particular domain. This segmentation improves the capture of domain-specific knowledge by providing dedicated representation spaces, while the modular structure manages system complexity through organized, reusable components that can be independently developed and maintained.
4Manufacturing precision
If domain-specific feature spaces are created for each domain, then the distinction of user behaviors and preferences is improved, but the implementation cost increases
Solution Approach 1:
The patent segments the feature representation system into domain-specific feature spaces, enabling improved distinction of user behaviors and preferences within each domain through specialized representation. The modular architecture manages implementation cost by allowing independent development and deployment of individual domain feature spaces, reducing the burden of system-wide redesign.
Solution Approach 2:
The patent applies local quality by optimizing each domain-specific feature space for its particular data characteristics and user behavior patterns. This localized optimization improves the distinction of user behaviors and preferences within each domain while keeping implementation costs manageable through focused, domain-specific development rather than comprehensive system redesign.
Data Source
AI summary
There are proposed methods, devices, and computer program products for extending a feature space of a data sample. In the method, a global representation is obtained for a feature in a plurality of features of the data sample. A local representation is obtained for the feature based on a classifying criterion for classifying the data sample into one of a plurality of predefined domains. A representation is generated for the feature of the data sample based on the global representation and the local representation. With these implementations, an exclusive feature space may be created for each domain identified by the classifying criterion, which is dedicated to capturing domain-specific knowledge and characteristics.