Label-independent hyperparameter-free self-supervised single-view deep subspace clustering (2504.18179v1)

Abstract: Deep subspace clustering (DSC) algorithms face several challenges that hinder their widespread adoption across variois application domains. First, clustering quality is typically assessed using only the encoder's output layer, disregarding valuable information present in the intermediate layers. Second, most DSC approaches treat representation learning and subspace clustering as independent tasks, limiting their effectiveness. Third, they assume the availability of a held-out dataset for hyperparameter tuning, which is often impractical in real-world scenarios. Fourth, learning termination is commonly based on clustering error monitoring, requiring external labels. Finally, their performance often depends on post-processing techniques that rely on labeled data. To address this limitations, we introduce a novel single-view DSC approach that: (i) minimizes a layer-wise self expression loss using a joint representation matrix; (ii) optimizes a subspace-structured norm to enhance clustering quality; (iii) employs a multi-stage sequential learning framework, consisting of pre-training and fine-tuning, enabling the use of multiple regularization terms without hyperparameter tuning; (iv) incorporates a relative error-based self-stopping mechanism to terminate training without labels; and (v) retains a fixed number of leading coefficients in the learned representation matrix based on prior knowledge. We evaluate the proposed method on six datasets representing faces, digits, and objects. The results show that our method outperforms most linear SC algorithms with careffulyl tuned hyperparameters while maintaining competitive performance with the best performing linear appoaches.

An Expert Review of Label-independent Hyperparameter-free Self-supervised Single-view Deep Subspace Clustering

Deep subspace clustering (DSC) has emerged as a potent approach in the field of data segmentation, with the potential to address the unsupervised clustering of data points based on their subspaces. The paper "Label-independent Hyperparameter-free Self-supervised Single-view Deep Subspace Clustering" explores notable limitations of existing DSC techniques and proposes a novel methodology, LIHFSS-SVDSC, to circumvent these challenges.

Challenges in Existing Methods

Current DSC algorithms primarily evaluate clustering performance based on the encoder’s final output layer, yet intermediate layers potentially harbor significant informative value. This oversight leads to suboptimal clustering efficacy. Furthermore, a key shortcoming of prevalent DSC models is their bifurcated approach to representation learning and clustering, often treating them as distinct tasks. Moreover, these models require a holdout dataset for tuning hyperparameters—a practice infeasible in many real-world scenarios—alongside reliance on external labels for learning termination and clustering error monitoring.

Proposed Methodology

The LIHFSS-SVDSC introduces an innovative DSC framework devoid of hyperparameters and label dependencies, employing a multi-phase sequential learning process. The approach deploys:

1. Layer-wise Self-expression Loss: Unlike traditional models, this method capitalizes on layer-wise information by constructing a joint representation matrix that enhances clustering quality through the optimization of a subspace-structured norm.
2. Sequential Learning Framework: This encapsulates pre-training and fine-tuning stages leveraging multiple regularization terms, dispensing the need for hyperparameter adjustment.
3. Self-stopping Mechanism: The algorithm employs a relative error-based self-stopping rule, facilitating label-independent training termination. The method posits an innate self-correcting feature, maintaining a fixed number of leading coefficients in the representation matrix based on prior subspace knowledge.

Evaluation and Performance

The LIHFSS-SVDSC method was evaluated on datasets encompassing faces, digits, and objects, where it outperformed several linear SC algorithms even when those algorithms employed fine-tuned hyperparameters. This performance starkly underscores the utility of leveraging multi-layer representations and subspace-structuring. However, LIHFSS-SVDSC’s performance vis-à-vis non-linear DSC algorithms with optimized hyperparameters remained competitive, evidencing its robustness even in the absence of tailored hyperparameters.

Implications and Future Directions

The paper marks a substantial step forward in the field of unsupervised deep learning by decoupling clustering efficacy from hyperparameter dependencies. This contribution is especially impactful in deployment scenarios lacking labeled datasets or pre-established hyperparmeters, paving the way toward a truly autonomous clustering framework. The practical implications extend to fields where labeled data is scarce or inaccessible.

Further work could explore simultaneous optimization strategies for multi-loss functions that align closely with real-time data features. The integration of internal clustering quality metrics or target distributions might also enhance clustering performance. This could imbue the LIHFSS-SVDSC framework with broader applicability across diverse and complex datasets, optimizing clustering accuracy without compromising on the principled autonomy of the approach.