Manifold Learning Localization Model for Multi-Modal Sensing
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Solution Overview
Problem
Machine learning model architectures for localization are often data-modality specific, limiting their applicability across different input data types, and require significant time and processing power for training, making them inefficient for varied sensory systems.
Innovation Solution
A modality-agnostic machine learning model training architecture that uses manifold learning and optimal transportation to map input data from a high-dimensional ambient space to a lower-dimensional intrinsic space and then to a target space, such as a topological map, without requiring separate models for each data modality, allowing for joint learning of intrinsic embeddings and transportation mechanisms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If modality-specific machine learning models are used for localization, then accuracy for a specific data type is improved, but adaptability to different sensory systems deteriorates
Solution Approach 1:
The patent implements a universal machine learning model architecture that can process multiple data modalities (video, audio, wireless signals, images) through a common framework. The model uses modality-agnostic feature extraction layers followed by task-specific heads, allowing the same base architecture to serve multiple sensing types without requiring separate models for each modality.
Solution Approach 2:
The patent introduces an intermediate feature representation layer that acts as a mediator between raw input data and task-specific outputs. This intermediate layer transforms diverse input modalities into a unified feature space, enabling the model to handle different sensory systems through a common processing pathway while maintaining the ability to specialize for specific tasks.
2Measurement precision
If modality-specific machine learning models are trained separately, then performance for each specific task is improved, but processing time and computational resources increase
Solution Approach 1:
The patent merges multiple modality-specific models into a single unified model that processes all input types through shared layers. By combining the training of what would otherwise be separate models into one joint training process, the system reduces total training time and computational resources while maintaining specialized performance through task-specific output heads.
Solution Approach 2:
The universal model architecture allows a single model to perform multiple localization tasks across different modalities, eliminating the need to train separate models for each data type. This multi-functional approach significantly reduces training time and computational overhead while maintaining specialized performance through the shared feature extraction capabilities.
3Measurement precision
If modality-specific machine learning models are trained separately, then specialization for each data type is improved, but device complexity increases
Solution Approach 1:
The patent employs a single universal model architecture that handles multiple data modalities and tasks, replacing what would otherwise require multiple separate specialized models. This unified approach reduces device complexity by consolidating multiple models into one while maintaining specialization capabilities through the model's ability to process different input types and produce task-specific outputs.
Solution Approach 2:
The universal model is segmented into modular components including shared feature extraction layers and task-specific output heads. This segmentation allows the model to maintain specialization for different tasks and modalities through distinct output modules while sharing common processing infrastructure, thereby reducing overall complexity compared to having entirely separate models for each function.
Data Source
AI summary
Certain aspects of the present disclosure provide techniques for training and inferencing with machine learning localization models. In one aspect, a method, includes training a machine learning model based on input data for performing localization of an object in a target space, including: determining parameters of a neural network configured to map samples in an input space based on the input data to samples in an intrinsic space; and determining parameters of a coupling matrix configured to transport the samples in the intrinsic space to the target space.