A Sparse PCA Approach to Clustering
arXiv:1602.05236v11 citations
Originality Synthesis-oriented
AI Analysis
This is an incremental contribution to clustering methods for specific statistical models.
The paper tackles clustering in Gaussian mixture models using sparse PCA and compares it with IF-PCA, also addressing cases with non-diagonal covariance matrices, but no concrete results or numbers are provided.
We discuss a clustering method for Gaussian mixture model based on the sparse principal component analysis (SPCA) method and compare it with the IF-PCA method. We also discuss the dependent case where the covariance matrix $Σ$ is not necessarily diagonal.