Feature Embeddings From Co-occurrence Matrix Submatrices
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Solution Overview
Problem
Existing feature embedding systems generate embeddings based solely on observed co-occurrences, failing to accurately represent the original feature space and being computationally inefficient, especially when dealing with large corpora and rare features.
Innovation Solution
The method involves constructing submatrices from a feature co-occurrence matrix, iteratively adjusting initial embedding vectors using stochastic gradient descent to minimize the error between predicted and observed point-wise mutual information, allowing for the consideration of both observed and unobserved co-occurrences, and optimizing computational efficiency by distributing calculations across multiple nodes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If feature embeddings are generated based solely on observed co-occurrences, then the generation process is simple, but the accuracy and completeness of representing the original feature space deteriorates
Solution Approach 1:
The patent segments the feature co-occurrence matrix into multiple submatrices based on different feature vocabularies (e.g., content features, style features, domain features). Each submatrix is processed separately to generate embeddings, allowing the system to capture different aspects of feature relationships while maintaining computational tractability. This segmentation resolves the contradiction by enabling comprehensive feature space representation without requiring processing of the entire large-scale co-occurrence matrix at once.
Solution Approach 2:
The patent transforms the traditional approach by introducing a new dimensional perspective: instead of directly generating embeddings from raw co-occurrence data, it first constructs a structured feature co-occurrence matrix that explicitly models relationships between different feature types. This dimensional transformation allows the system to capture complex feature interactions while maintaining computational efficiency through the subsequent submatrix decomposition.
2Measurement precision
If feature embeddings are generated using comprehensive co-occurrence analysis, then the quality of embeddings improves, but computational time and resources increase
Solution Approach 1:
The patent divides the large feature co-occurrence matrix into multiple smaller submatrices based on feature vocabulary categories. Each submatrix can be processed independently and in parallel, significantly reducing the computational time required for embedding generation while maintaining the quality of embeddings by preserving important feature relationships within each category.
Solution Approach 2:
The patent processes only the most relevant submatrices containing significant feature co-occurrence patterns, rather than uniformly processing all possible feature combinations. This selective approach focuses computational resources on the most informative parts of the feature space, achieving high-quality embeddings with reduced computational time by avoiding processing of sparse or less important feature relationships.
3Productivity
If the entire feature co-occurrence matrix is processed at once, then computational efficiency is maintained, but the system cannot handle large-scale datasets and rare features
Solution Approach 1:
The patent segments the feature co-occurrence matrix into manageable submatrices organized by feature vocabulary categories. This segmentation enables the system to handle large-scale datasets by processing each submatrix independently, allowing rare features within specific categories to be captured without being lost in the noise of the entire large-scale matrix. The modular approach maintains computational efficiency while significantly improving adaptability to large and diverse datasets.
Solution Approach 2:
The patent introduces a categorical dimension to organize features into different vocabularies (content, style, domain, etc.). This dimensional organization allows the system to handle large datasets by grouping related features together, making rare features within each category more detectable and meaningful. The categorical structure enables efficient processing while improving the system's ability to adapt to diverse and large-scale datasets.
4Device complexity
If feature embeddings are generated without considering unobserved co-occurrences, then the model is simpler, but the completeness of feature representation deteriorates
Solution Approach 1:
The patent performs preliminary analysis of the feature co-occurrence matrix to identify patterns and relationships before generating embeddings. By pre-processing the data to establish feature vocabulary categories and organize co-occurrence patterns, the system can later infer unobserved relationships based on established patterns within each category. This preliminary action enables the model to capture unobserved co-occurrences without significantly increasing complexity, as the structural framework is already in place.
Solution Approach 2:
The patent incorporates feedback mechanisms where the embedding generation process iteratively refines the representation of feature relationships. By using the generated embeddings to inform subsequent processing and adjusting for unobserved co-occurrences based on patterns from observed data, the system improves the completeness of feature representation. The feedback loop allows the model to compensate for unobserved relationships while maintaining reasonable model complexity through iterative refinement rather than complex structural changes.
Data Source
AI summary
Methods, and systems, including computer programs encoded on computer storage media for generating compressed representations from a co-occurrence matrix. A method includes obtaining a set of sub matrices of a co-occurrence matrix, where each row of the co-occurrence matrix corresponds to a feature from a first feature vocabulary and each column of the co-occurrence matrix corresponds to a feature from a second feature vocabulary; selecting a sub matrix, wherein the sub matrix is associated with a particular row block and column block of the co-occurrence matrix; assigning respective d-dimensional initial row and column embedding vectors to each row and column from the particular row and column blocks, respectively; and determining a final row embedding vector and a final column embedding vector by iteratively adjusting the initial row embedding vectors and the initial column embedding vectors using the co-occurrence matrix.