Multimodal Embedding Projection for Lower-Dimensional Similarity Preservation
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Solution Overview
Problem
Existing machine learning systems face challenges in efficiently reducing the dimensionality of embeddings without significant loss of performance, particularly in large-scale applications, leading to high computational and storage costs.
Innovation Solution
A method involving the use of projection vectors optimized through a training process that minimizes the difference between original and reduced similarity matrices, optionally using selective masking techniques, to preserve the integrity of the embedding space while reducing dimensionality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If embeddings are used to represent high-dimensional data in lower-dimensional space, then storage and computation efficiency are improved, but performance loss occurs due to dimensionality reduction
Solution Approach 1:
The patent transforms embeddings from high-dimensional space to low-dimensional space through projection vectors, achieving dimensionality reduction while preserving essential information. The projection vectors act as a transformation bridge between dimensional spaces, allowing efficient storage and computation without significant performance degradation.
Solution Approach 2:
The patent optimizes projection vectors by minimizing a loss function that measures the difference between original and projected similarity matrices. This parameter optimization ensures that the reduced-dimensional embeddings maintain the essential relationships and performance characteristics of the original high-dimensional embeddings.
2Quantity of substance
If the dimensionality of embeddings is reduced, then storage requirements and computational costs are decreased, but information loss occurs in the embedding space
Solution Approach 1:
The patent employs a feedback mechanism through loss function optimization, where the projected similarity matrix is compared against the original similarity matrix. This feedback loop guides the optimization of projection vectors to minimize information loss during dimensionality reduction, ensuring that critical relationships are preserved in the reduced embedding space.
Solution Approach 2:
By optimizing projection vectors as learnable parameters, the system dynamically adjusts the transformation to preserve maximum information. The parameter optimization process ensures that the reduced-dimensional embeddings retain essential information while achieving compact representation for efficient storage.
3Manufacturing precision
If projection vectors are optimized through training, then the quality of reduced-dimensional embeddings is improved, but computational resources and training time are increased
Solution Approach 1:
The patent creates a simplified copy of the embedding transformation process using projection vectors. Instead of working with full high-dimensional embeddings throughout the system, it uses optimized low-dimensional projections that replicate the essential functionality, reducing computational resource requirements while maintaining quality.
Data Source
AI summary
Apparatus and method for reducing the dimensionality of embeddings in a machine learning (ML) system. In some embodiments, original embeddings from a pre-trained deep learning model are extracted for a set of data, the original embeddings having an initial dimensionality. A set of projection vectors are initialized with a specified dimensionality smaller than the initial dimensionality. A neural network is used to optimize the projection vectors by minimizing a loss function based on a similarity matrix. The set of original embeddings are thereafter projected onto the optimized projection vectors to obtain a set of reduced-dimensional embeddings with the specified dimensionality. A neural network of an ML system is thereafter configured using the reduced-dimensional embeddings, such as by a training operation to duplicate operation of the deep learning model in a smaller latent space. The original embeddings may be single modal or multimodal embeddings.