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PyTorch implementation of Self-training approch for short text clustering

License: MIT License

Makefile 0.37% Python 99.63%
autoencoder clustering deep-learning machine-learning pytorch representation-learning self-training short-text stc deep-clustering sentence-embeddings

torchstc's Introduction

PyTorch-Short-Text-Clustering

PyTorch version of Self-training approch for short text clustering

Image of STC Arch

Self-training Steps

Step 1 - Train autoencoder

The aim of an auto-encoder is to have an output as close as possible to the input. In our study, the mean square error is used to measure the reconstruction loss after data compression and decompression. The autoencoder architecture can be seen in the figure below

Image of STC Arch

Step 2 - Initialize centroids through KMeans-like clustering​

Initialize cluster centroids involve apply clustering algorithme like K-Means over latent vector space.

Image of STC Arch

Step 3 - Joint Optimization of Feature Representations and Cluster Assignments through Self-training

Using our cluster centroids, to compute a fuzzy partition of data Q is computed with the following. It's kind of like similarity betweens all data point and centoids in terme of probability distribution.

Then, an auxiliary probability distribution P, stricter is computed that put more emphasis on data points assigned with high confidence, in the aim to improve cluster purity.

Image of STC Arch

Visualization

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Demo

Follow this demo to know how to run scripts.

torchstc's People

Contributors

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