i-IF-Learn: Iterative Feature Selection and Unsupervised Learning for High-Dimensional Complex Data
arXiv:2603.24025v1 Announce Type: new Abstract: Unsupervised learning of high-dimensional data is challenging due to irrelevant or noisy features obscuring underlying structures. It's common that only a few features, called the influential features, meaningfully define the clusters. Recovering these influential features is helpful in data interpretation and clustering. We propose i-IF-Learn, an iterative unsupervised framework that jointly performs feature selection and clustering. Our core innovation is an adaptive feature selection statistic that effectively combines pseudo-label supervision with unsupervised signals, dynamically adjusting based on intermediate label reliability to mitigate error propagation common in iterative frameworks. Leveraging low-dimensional embeddings (PCA or Laplacian eigenmaps) followed by $k$-means, i-IF-Learn simultaneously outputs influential feature subset and clustering labels. Numerical experiments on gene microarray and single-cell RNA-seq datasets
arXiv:2603.24025v1 Announce Type: new Abstract: Unsupervised learning of high-dimensional data is challenging due to irrelevant or noisy features obscuring underlying structures. It's common that only a few features, called the influential features, meaningfully define the clusters. Recovering these influential features is helpful in data interpretation and clustering. We propose i-IF-Learn, an iterative unsupervised framework that jointly performs feature selection and clustering. Our core innovation is an adaptive feature selection statistic that effectively combines pseudo-label supervision with unsupervised signals, dynamically adjusting based on intermediate label reliability to mitigate error propagation common in iterative frameworks. Leveraging low-dimensional embeddings (PCA or Laplacian eigenmaps) followed by $k$-means, i-IF-Learn simultaneously outputs influential feature subset and clustering labels. Numerical experiments on gene microarray and single-cell RNA-seq datasets show that i-IF-Learn significantly surpasses classical and deep clustering baselines. Furthermore, using our selected influential features as preprocessing substantially enhances downstream deep models such as DeepCluster, UMAP, and VAE, highlighting the importance and effectiveness of targeted feature selection.
Executive Summary
The article 'i-IF-Learn: Iterative Feature Selection and Unsupervised Learning for High-Dimensional Complex Data' proposes an innovative unsupervised framework for jointly performing feature selection and clustering in high-dimensional data. The adaptive feature selection statistic effectively combines pseudo-label supervision with unsupervised signals, mitigating error propagation in iterative frameworks. Numerical experiments demonstrate the superiority of i-IF-Learn over classical and deep clustering baselines, and its ability to enhance downstream deep models. This framework has the potential to significantly improve data interpretation and clustering in various fields, including genomics and single-cell RNA-seq analysis. The article's findings are significant, and the proposed method is well-suited for tackling the challenges of high-dimensional data analysis.
Key Points
- ▸ i-IF-Learn is an iterative unsupervised framework for feature selection and clustering
- ▸ Adaptive feature selection statistic combines pseudo-label supervision with unsupervised signals
- ▸ Significantly surpasses classical and deep clustering baselines in numerical experiments
Merits
Effective Feature Selection
The adaptive feature selection statistic effectively identifies influential features, improving data interpretation and clustering
Improved Downstream Models
Using selected influential features as preprocessing enhances the performance of downstream deep models
Robustness to Error Propagation
The adaptive feature selection statistic dynamically adjusts based on intermediate label reliability, mitigating error propagation
Demerits
Limited Real-World Applications
The article focuses on simulated datasets, and it is unclear how well the method generalizes to real-world data
Computational Complexity
The iterative framework may be computationally expensive, particularly for large datasets
Expert Commentary
The article presents a novel and effective approach to high-dimensional data analysis. The proposed framework, i-IF-Learn, demonstrates superior performance over classical and deep clustering baselines, and its ability to enhance downstream deep models is a significant advantage. However, the article's limitations, including the use of simulated datasets and potential computational complexity, should be addressed in future work. Overall, the article makes a significant contribution to the field of high-dimensional data analysis and has the potential to impact various applications.
Recommendations
- ✓ Future work should focus on applying i-IF-Learn to real-world datasets and evaluating its performance in various applications
- ✓ The authors should investigate methods to reduce computational complexity and make the framework more scalable
Sources
Original: arXiv - cs.LG
