Summary of Robust Clustering on High-dimensional Data with Stochastic Quantization, by Anton Kozyriev et al.
Robust Clustering on High-Dimensional Data with Stochastic Quantization
by Anton Kozyriev, Vladimir Norkin
First submitted to arxiv on: 3 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Optimization and Control (math.OC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper addresses the limitations of conventional vector quantization algorithms, particularly K-Means and its variant K-Means++, and investigates the Stochastic Quantization (SQ) algorithm as a scalable alternative for high-dimensional unsupervised and semi-supervised learning tasks. The authors highlight the inefficiency of traditional clustering algorithms in handling large-scale datasets, which often require loading all data samples into memory. They propose the SQ algorithm, which provides strong theoretical convergence guarantees, making it a robust alternative for clustering tasks. The authors demonstrate the computational efficiency and rapid convergence of the algorithm on an image classification problem with partially labeled data, comparing model accuracy across various ratios of labeled to unlabeled data. Additionally, they employ a Triplet Network to encode images into low-dimensional representations in a latent space, which serve as a basis for comparing the efficiency of both the Stochastic Quantization algorithm and traditional quantization algorithms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding a better way to group things (like pictures) together based on how similar they are. The current methods are not very good at handling big groups of things, so the authors came up with a new method called Stochastic Quantization (SQ). This method is more efficient and works well even when some of the things are missing labels. They tested it on a picture recognition task and showed that it’s faster and better than other methods. |
Keywords
» Artificial intelligence » Clustering » Image classification » K means » Latent space » Quantization » Semi supervised » Unsupervised
