Maximum Joint Probability With Multiple Representations for Clustering.

Area of Science:

• Machine Learning
• Unsupervised Learning
• Data Clustering

Background:

• Classical generative models aim to maximize data distribution p(X).
• Real-world data often presents multiple representations due to transformations and measurement variations.
• Existing models struggle to leverage prior data distribution information to effectively distinguish these representations.

Purpose of the Study:

• Propose a novel clustering framework to maximize the joint probability of data and parameters.
• Integrate prior information about data distribution p(X) to enhance representation discrimination.
• Develop a specific clustering model for multiple kernels and multiple views.

Main Methods:

• Formulated a new clustering framework maximizing joint probability P(Data, Parameters).
• Utilized prior distribution to assess the rationality of diverse data representations.
• Derived a specific clustering model incorporating multiple kernels and multiple views.

Main Results:

• The proposed framework allows prior distribution to effectively measure the rationality of different data representations.
• K-means clustering is identified as a special case within this framework.
• A specific multi-kernel, multi-view clustering model validated the framework's effectiveness.

Conclusions:

• The novel framework successfully integrates prior data distribution knowledge for improved representation handling in unsupervised learning.
• The derived multi-kernel, multi-view model demonstrates the practical applicability and validity of the proposed approach.
• This work offers a more robust method for clustering data with multiple, potentially ambiguous, representations.