Exploring a Principled Framework for Deep Subspace Clustering

• URL: http://arxiv.org/abs/2503.17288v1
• Date: Fri, 21 Mar 2025 16:38:37 GMT
• Title: Exploring a Principled Framework for Deep Subspace Clustering
• Authors: Xianghan Meng, Zhiyuan Huang, Wei He, Xianbiao Qi, Rong Xiao, Chun-Guang Li,
• Abstract summary: We present a Principled fRamewOrk for Deep Subspace Clustering (PRO-DSC)<n>PRO-DSC is designed to learn structured representations and self-expressive coefficients in a unified manner.<n>We prove that the learned optimal representations under certain condition lie on a union of subspaces.
• Score: 9.347670574036563
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Subspace clustering is a classical unsupervised learning task, built on a basic assumption that high-dimensional data can be approximated by a union of subspaces (UoS). Nevertheless, the real-world data are often deviating from the UoS assumption. To address this challenge, state-of-the-art deep subspace clustering algorithms attempt to jointly learn UoS representations and self-expressive coefficients. However, the general framework of the existing algorithms suffers from a catastrophic feature collapse and lacks a theoretical guarantee to learn desired UoS representation. In this paper, we present a Principled fRamewOrk for Deep Subspace Clustering (PRO-DSC), which is designed to learn structured representations and self-expressive coefficients in a unified manner. Specifically, in PRO-DSC, we incorporate an effective regularization on the learned representations into the self-expressive model, prove that the regularized self-expressive model is able to prevent feature space collapse, and demonstrate that the learned optimal representations under certain condition lie on a union of orthogonal subspaces. Moreover, we provide a scalable and efficient approach to implement our PRO-DSC and conduct extensive experiments to verify our theoretical findings and demonstrate the superior performance of our proposed deep subspace clustering approach. The code is available at https://github.com/mengxianghan123/PRO-DSC.

Related papers

• Label-independent hyperparameter-free self-supervised single-view deep subspace clustering [0.0]
  Deep subspace clustering (DSC) algorithms face several challenges that hinder their widespread adoption across domains. We introduce a novel single-view DSC approach that minimizes a layer-wise self expression loss using a joint representation matrix. We evaluate the proposed method on six datasets representing faces, digits, and objects.
  arXiv  Detail & Related papers  (2025-04-25T08:54:34Z)
• Adversarial Subspace Generation for Outlier Detection in High-Dimensional Data [41.09135146101542]
  We introduce Myopic Subspace Theory (MST), a new theoretical framework that mathematically formulates the Multiple Views effect. Based on MST, we introduce V-GAN, a generative method trained to solve such an optimization problem. Experiments on 42 real-world datasets show that using V-GAN leads to a significant increase in one-class classification performance.
  arXiv  Detail & Related papers  (2025-04-10T07:40:02Z)
• Learning Identifiable Structures Helps Avoid Bias in DNN-based Supervised Causal Learning [56.22841701016295]
  Supervised Causal Learning (SCL) is an emerging paradigm in this field.<n>Existing Deep Neural Network (DNN)-based methods commonly adopt the "Node-Edge approach"
  arXiv  Detail & Related papers  (2025-02-15T19:10:35Z)
• GCC: Generative Calibration Clustering [55.44944397168619]
  We propose a novel Generative Clustering (GCC) method to incorporate feature learning and augmentation into clustering procedure. First, we develop a discrimirative feature alignment mechanism to discover intrinsic relationship across real and generated samples. Second, we design a self-supervised metric learning to generate more reliable cluster assignment.
  arXiv  Detail & Related papers  (2024-04-14T01:51:11Z)
• Unfolding ADMM for Enhanced Subspace Clustering of Hyperspectral Images [43.152314090830174]
  We introduce an innovative clustering architecture for hyperspectral images (HSI) by unfolding an iterative solver based on the Alternating Direction Method of Multipliers (ADMM) for sparse subspace clustering. Our approach captures well the structural characteristics of HSI data by employing the K nearest neighbors algorithm as part of a structure preservation module.
  arXiv  Detail & Related papers  (2024-04-10T15:51:46Z)
• Preventing Collapse in Contrastive Learning with Orthonormal Prototypes (CLOP) [0.0]
  CLOP is a novel semi-supervised loss function designed to prevent neural collapse by promoting the formation of linear subspaces among class embeddings. We show that CLOP enhances performance, providing greater stability across different learning rates and batch sizes.
  arXiv  Detail & Related papers  (2024-03-27T15:48:16Z)
• Structure-Aware Feature Generation for Zero-Shot Learning [108.76968151682621]
  We introduce a novel structure-aware feature generation scheme, termed as SA-GAN, to account for the topological structure in learning both the latent space and the generative networks. Our method significantly enhances the generalization capability on unseen-classes and consequently improve the classification performance.
  arXiv  Detail & Related papers  (2021-08-16T11:52:08Z)
• Deep Clustering by Semantic Contrastive Learning [67.28140787010447]
  We introduce a novel variant called Semantic Contrastive Learning (SCL) It explores the characteristics of both conventional contrastive learning and deep clustering. It can amplify the strengths of contrastive learning and deep clustering in a unified approach.
  arXiv  Detail & Related papers  (2021-03-03T20:20:48Z)
• Towards Uncovering the Intrinsic Data Structures for Unsupervised Domain Adaptation using Structurally Regularized Deep Clustering [119.88565565454378]
  Unsupervised domain adaptation (UDA) is to learn classification models that make predictions for unlabeled data on a target domain. We propose a hybrid model of Structurally Regularized Deep Clustering, which integrates the regularized discriminative clustering of target data with a generative one. Our proposed H-SRDC outperforms all the existing methods under both the inductive and transductive settings.
  arXiv  Detail & Related papers  (2020-12-08T08:52:00Z)
• A Critique of Self-Expressive Deep Subspace Clustering [23.971512395191308]
  Subspace clustering is an unsupervised clustering technique designed to cluster data that is supported on a union of linear subspaces. We show that there are a number of potential flaws with this approach which have not been adequately addressed in prior work.
  arXiv  Detail & Related papers  (2020-10-08T00:14:59Z)
• Deep Metric Structured Learning For Facial Expression Recognition [58.7528672474537]
  We propose a deep metric learning model to create embedded sub-spaces with a well defined structure. A new loss function that imposes Gaussian structures on the output space is introduced to create these sub-spaces. We experimentally demonstrate that the learned embedding can be successfully used for various applications including expression retrieval and emotion recognition.
  arXiv  Detail & Related papers  (2020-01-18T06:23:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.