A Systematic Review and Categorization of Loss Functions in Deep Clustering
Abstract
Clustering techniques perform the task of discovering underlying patterns and structures in data. They play a crucial role in fields such as big data analytics, recommendation systems, and medical diagnostics, driving intelligent decisionmaking and efficient data processing. Deep clustering, with its strong ability to extract features, effectively overcomes the shortcomings of traditional clustering techniques, making it a prominent area of current research. Among these methods, the loss function, as the core component of deep clustering, guides the model in optimizing data representation, ensuring the effectiveness and stability of feature extraction from high-dimensional and complex data. However, existing studies primarily focus on the deep learning architecture, with few offering a systematic analysis from the perspective of loss functions. This paper reviews the current state of deep clustering research from the loss function viewpoint and categorizes relevant algorithms based on the characteristics of their loss functions. By analyzing the strengths and weaknesses of various loss functions, four essential elements for an effective loss function are proposed: information preservation, balance, robustness, and scalability. Future research directions are explored with respect to these four aspects.
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Introduction
Clustering is an unsupervised learning method aimed at partitioning a dataset into several groups or clusters such that samples within the same group exhibit high similarity, while samples from different groups show low similarity, following the principle of "birds of a feather flock together." Clustering algorithms do not rely on pre-labeled training data; instead, they uncover the intrinsic similarities within data by analyzing its structure.
As a significant area in machine learning, clustering plays an indispensable role in real-world applications. When the data labels are unknown or difficult to obtain, clustering helps in understanding the inherent structure of the data and uncovering patterns and trends within. It can also be applied in anomaly detection to identify outliers that deviate significantly from the rest of the samples. In image segmentation and object recognition, clustering techniques can group similar regions or objects in images, improving the accuracy of image analysis. Clustering is a versatile tool that simplifies complexity, reveals underlying relationships, and provides powerful support for decision-making and problem-solving. With continuous technological advancements, the application potential of clustering analysis in various fields will continue to be explored and expanded.