Representation Learning Apparatus for Interest Feature Clustering
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Solution Overview
Problem
Existing representation learning methods fail to effectively distinguish between interest and non-interest features in complex data, leading to poor clustering accuracy and feature suppression.
Innovation Solution
A representation learning apparatus that calculates latent vectors for both target and non-interest features using separate machine learning models, with a loss function that enhances interest features while suppressing non-interest features by adjusting similarities between latent vectors and representative values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single machine learning model is used for representation learning, then the model complexity is low, but the clustering accuracy deteriorates due to inability to distinguish interest and non-interest features
Solution Approach 1:
The patent divides the representation learning task into two separate machine learning models: a first model for learning interest features and a second model for learning non-interest features. This segmentation allows each model to specialize in specific feature types, improving clustering accuracy by preventing non-interest features from interfering with interest feature learning, while keeping individual model complexity manageable.
2Loss of information
If traditional representation learning is applied to complex data, then the processing is simple, but non-interest features are suppressed leading to loss of information
Solution Approach 1:
The patent segments feature learning into two parallel processes: one for interest features and one for non-interest features. By calculating separate latent vectors for each feature type using dedicated models, the system preserves non-interest feature information that would otherwise be suppressed or lost in traditional single-model approaches.
Solution Approach 2:
The patent introduces an intermediary mechanism in the loss function that balances the contribution of interest and non-interest features. This intermediary control allows the system to prevent harmful suppression of non-interest features while still emphasizing interest features for clustering, thereby reducing overall information loss.
3Measurement precision
If interest features are emphasized in representation learning, then clustering performance improves, but non-interest features are suppressed
Solution Approach 1:
The patent segments the feature learning process into two independent but parallel streams: one stream learns interest features using a first machine learning model, while another stream learns non-interest features using a second model. This segmentation enables simultaneous optimization for clustering accuracy while preserving non-interest feature information in separate latent representations.
Solution Approach 2:
The patent applies local quality by assigning different learning objectives and model parameters to different feature types. The first model is optimized for interest features with clustering-focused loss, while the second model handles non-interest features with appropriate regularization, allowing each part of the system to have specialized properties suited to its function.
Data Source
AI summary
A representation learning apparatus executing: calculating a latent vector Sx in a latent space of the target data x using a first model parameter, calculate a non-interest latent vector Zx in a latent space of an non-interest feature included in the target data x and a non-interest latent vector Zb in the latent space of a non-interest data using a second model parameter, calculate a similarity S1 obtained by correcting a similarity between the latent vector Sx and its representative value S′x by a similarity between the latent vector Zx and its representative value Z′x, and a similarity S2 between the latent vector Zb and its representative value Z′b, and update the first and/or the second model parameter based on the loss function including the similarity S1 and S2.