Clustering Deep Learning Embeddings for Feature Selection
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Solution Overview
Problem
Existing machine learning models struggle to effectively combine deep learning embeddings with baseline features, often leading to performance issues due to overwhelming complexity or failure to control for known associations.
Innovation Solution
The method involves clustering deep learning embeddings into distinct clusters, generating cluster quantitation vectors for each training case, evaluating the performance impact of these clusters when combined with baseline features, and selectively using the most beneficial clusters as machine-learning features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If full embedding feature data is provided to the model, then the model captures rich information about input cases, but the baseline feature data is overwhelmed and the model does not exhibit performance improvement
Solution Approach 1:
The embedding feature data is segmented into multiple clusters based on similarity, where each cluster represents a group of embeddings with common characteristics. This segmentation transforms the overwhelming full embedding data into manageable cluster representations that preserve essential information while reducing dominance over baseline features.
Solution Approach 2:
The method extracts only the most relevant embedding clusters that provide performance improvement when combined with baseline features. By evaluating each cluster's contribution and selecting only those that add value, the system extracts the essential information from embeddings without including redundant or overwhelming data.
2Adaptability or versatility
If only learned features (embeddings) are provided to the model, then the model processes deep features, but the model does not control for known associations and shows no improvement over baseline features alone
Solution Approach 1:
The system dynamically determines which embedding clusters to use based on their performance contribution. Rather than statically using all embeddings or none, the method adaptively selects clusters that provide genuine performance improvement when combined with baseline features, creating a dynamic feature selection process.
Solution Approach 2:
The method employs feedback by evaluating the performance impact of each embedding cluster when combined with baseline features. Clusters are selected based on this performance feedback, ensuring that only those providing genuine improvement are included in the final model, thus controlling for known associations while leveraging deep features.
3Loss of information
If both baseline features and learned features are provided together, then the model receives comprehensive input, but the embedding data overwhelms the baseline features and performance improvement is not achieved
Solution Approach 1:
The comprehensive feature set is segmented into distinct baseline features and embedding clusters. This segmentation allows the model to process both types of features separately and combine them in a controlled manner, preventing the embedding data from overwhelming the baseline features while maintaining comprehensiveness.
Data Source
AI summary
Provided are systems and methods for the generation of machine-learning features by clustering deep learning embeddings and selecting embedding cluster data while controlling for known associations. In particular, a computing system can use a pre-trained machine learning model (e.g., an image embedding model) to obtain embeddings of input images. The computing system can train a clustering algorithm (e.g., a k-means algorithm) to cluster these embeddings into one of a number (e.g., k) clusters. The computing system can then perform a selection process to select one or more (e.g., the top n) clusters that boost performance in a prediction model (e.g., a logistic regression model) trained with a combination of the selected clusters and one or more baseline features. In such fashion, the computer system can enable an improved combination of extracted deep learned features and baseline features. This can maximize generalizable performance while controlling for known variables.