Joint Representation Learning and Clustering via Gradient-Based Manifold Optimization

• URL: http://arxiv.org/abs/2604.13484v1
• Date: Wed, 15 Apr 2026 05:18:27 GMT
• Title: Joint Representation Learning and Clustering via Gradient-Based Manifold Optimization
• Abstract summary: We propose a Manifold Learning Framework that learns dimensionality reduction and clustering simultaneously.<n>The proposed framework is able to jointly learn the parameters of a dimension reduction technique and cluster the data.<n>Our algorithm has better performance than popular clustering algorithms from the literature.
• Score: 1.7484661165784237
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Clustering and dimensionality reduction have been crucial topics in machine learning and computer vision. Clustering high-dimensional data has been challenging for a long time due to the curse of dimensionality. For that reason, a more promising direction is the joint learning of dimension reduction and clustering. In this work, we propose a Manifold Learning Framework that learns dimensionality reduction and clustering simultaneously. The proposed framework is able to jointly learn the parameters of a dimension reduction technique (e.g. linear projection or a neural network) and cluster the data based on the resulting features (e.g. under a Gaussian Mixture Model framework). The framework searches for the dimension reduction parameters and the optimal clusters by traversing a manifold,using Gradient Manifold Optimization. The obtained The proposed framework is exemplified with a Gaussian Mixture Model as one simple but efficient example, in a process that is somehow similar to unsupervised Linear Discriminant Analysis (LDA). We apply the proposed method to the unsupervised training of simulated data as well as a benchmark image dataset (i.e. MNIST). The experimental results indicate that our algorithm has better performance than popular clustering algorithms from the literature.

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