AI BriefingLG AI Research: A Review of Variational Deep Embedding-based Deep Clustering
Key point
This reviews Variational Autoencoder-based Deep Clustering techniques and key papers presented at NeurIPS 2021.
Details
Clustering is a technique that groups data based on similarity between data points without labels. Representative approaches include Spectral Clustering, which measures distances between samples, and the K-means algorithm, which optimizes centroids.
Spectral Clustering can effectively represent nonlinear data clusters, but it has the drawback that computational cost increases sharply as the amount of data grows, since distances between all data points must be calculated.
K-means is computationally efficient and widely used, but because it measures the distance between data and centroids based on the Euclidean metric, it tends to form clusters that are mainly circular in shape.
This review covers Deep Clustering, which applies the Variational Autoencoder (VAE) technique to clustering. In particular, based on the foundational study Variational Deep Embedding (VaDE), it introduces recent research presented at NeurIPS 2021.
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