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[2601.20154] Spectral Ghost in Representation Learning: from Component Analysis to Self-Supervised Learning
Summary
This paper explores the concept of spectral representation in self-supervised learning (SSL), aiming to unify various SSL methods and enhance their practical application in representation learning.
Why It Matters
As self-supervised learning continues to gain traction in machine learning, establishing a unified theoretical framework is crucial for advancing the field. This paper addresses the current lack of clarity in SSL methods, which could lead to more efficient algorithms and better understanding of representation learning.
Key Takeaways
- Self-supervised learning leverages unlabeled data for improved performance.
- A unified framework for representation learning is proposed to clarify existing methods.
- The paper emphasizes the need for theoretical foundations to guide algorithm design.
Computer Science > Machine Learning arXiv:2601.20154 (cs) [Submitted on 28 Jan 2026 (v1), last revised 12 Feb 2026 (this version, v2)] Title:Spectral Ghost in Representation Learning: from Component Analysis to Self-Supervised Learning Authors:Bo Dai, Na Li, Dale Schuurmans View a PDF of the paper titled Spectral Ghost in Representation Learning: from Component Analysis to Self-Supervised Learning, by Bo Dai and 2 other authors View PDF HTML (experimental) Abstract:Self-supervised learning (SSL) has improved empirical performance by unleashing the power of unlabeled data for practical applications. Specifically, SSL extracts the representation from massive unlabeled data, which will be transferred to a plenty of down streaming tasks with limited data. The significant improvement on diverse applications of representation learning has attracted increasing attention, resulting in a variety of dramatically different self-supervised learning objectives for representation extraction, with an assortment of learning procedures, but the lack of a clear and unified understanding. Such an absence hampers the ongoing development of representation learning, leaving a theoretical understanding missing, principles for efficient algorithm design unclear, and the use of representation learning methods in practice unjustified. The urgency for a unified framework is further motivated by the rapid growth in representation learning methods. In this paper, we are therefore compelled to develop ...
