Summary of An Approach Towards Learning K-means-friendly Deep Latent Representation, by Debapriya Roy
An Approach Towards Learning K-means-friendly Deep Latent Representation
by Debapriya Roy
First submitted to arxiv on: 29 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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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 The paper addresses the challenge of clustering in high-dimensional spaces, such as images. Traditional centroid-based approaches like K-means struggle with this problem. A common solution is to use autoencoders (AEs) that map data to a lower-dimensional latent space and then cluster in that space. Recent works have shown that jointly learning representations and cluster centroids is important. The proposed continuous variant of K-means uses the softmax function to learn parameters simultaneously using stochastic gradient descent (SGD). However, this approach differs from classical K-means, where the clustering space remains constant. This paper proposes an alternative approach that learns a clustering-friendly data representation and K-means-based cluster centers. Experimental results on benchmark datasets show improvements over previous approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper tries to solve a big problem in computer science called clustering. Clustering is like grouping similar things together, but it gets really hard when the things we’re trying to group are very complex, like images. Most old methods for doing this don’t work well with these kinds of data. A new approach uses something called autoencoders that help simplify the data so it’s easier to cluster. The paper also talks about a special kind of clustering called K-means, which is usually used in simpler situations. This paper proposes a new way to do K-means that works better than old methods. |
Keywords
» Artificial intelligence » Clustering » K means » Latent space » Softmax » Stochastic gradient descent
