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Updated: Jul 30, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

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Rank Flow Embedding for Unsupervised and Semi-Supervised Manifold Learning.

Lucas Pascotti Valem, Daniel Carlos Guimaraes Pedronette, Longin Jan Latecki

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|May 12, 2023

Summary

This summary is machine-generated.

Rank Flow Embedding (RFE) offers a novel manifold learning algorithm for unsupervised and semi-supervised learning. This method effectively handles limited labeled data for improved retrieval and classification tasks.

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Area of Science:

Computer Science
Machine Learning
Data Science

Background:

Advances in multimedia technology have led to vast collections, but labeled data for supervised learning remains scarce and costly.
The need for effective retrieval and classification methods is growing, yet supervised approaches are limited by data availability.

Purpose of the Study:

To introduce a novel manifold learning algorithm, Rank Flow Embedding (RFE), designed for unsupervised and semi-supervised learning scenarios.
To develop a method that can operate effectively with minimal or no labeled data, addressing the limitations of supervised approaches.

Main Methods:

The proposed Rank Flow Embedding (RFE) algorithm utilizes hypergraphs, Cartesian products, and connected components from manifold learning.
It computes context-sensitive embeddings refined through a rank-based processing flow, incorporating complementary contextual information.
Embeddings are generated for unsupervised retrieval and semi-supervised classification using Graph Convolutional Networks.

Main Results:

Experiments were conducted on 10 diverse collections, utilizing features from Convolutional Neural Networks (CNN) and Vision Transformer (ViT) models.
The Rank Flow Embedding (RFE) method demonstrated high effectiveness in unsupervised image retrieval, semi-supervised classification, and person re-identification (Re-ID).
RFE proved competitive or superior to state-of-the-art methods across various evaluated scenarios.

Conclusions:

The Rank Flow Embedding (RFE) algorithm presents a powerful solution for machine learning tasks with limited labeled data.
Its effectiveness in unsupervised and semi-supervised settings, including image retrieval and classification, highlights its versatility and performance.
RFE offers a significant advancement in handling multimedia data challenges where labeled datasets are constrained.