Answers.Body: <p>what you want is a combination of dimensionality reduction technique and clustering.</p>
  <p>Indeed, you can't do naive clustering as the feature space is way too large, thus you need some sort of lower dimensional representation. To do so, you can even naively use an autoencoder, and use the latent representation as a lower dimensional feature space.</p>
  <p>On such manifold, you can then apply usual clustering techniques, such as DBScan, Kmeans and so on</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2024-05-02T22:22:40.250Z
Answers.Text: Thanks @Alberto! Do you know of any algorithms that currently exist like this? Designing something from scratch is a bit above my paygrade
Answers.UserDisplayName: Tom
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2024-05-03T08:48:06.483Z
Answers.Text: @Tom i don't have any specific end to end tool for this problem, sorry
Answers.UserDisplayName: Alberto
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2024-05-02T21:17:44.157Z
Answers.LastActivityDate: 2024-05-02T21:17:44.157Z
Answers.UserDisplayName: Alberto
Body: <p>I'm wondering if there exists a CNN model (class of models?) designed to receive a single set of unlabelled, unpartitioned data, and attempt to infer classes. For instance, you could theoretically hand such a model a dataset containing some images of red squares and some images of blue circles, and the model would learn to distinguish the two types of images (perhaps by some kind of dimensionality reduction technique or Gaussian mixture model on the feature vectors).</p>
  <p>I feel like I've heard of similar techniques before, but my extensive Googling failed me. If there are similar but not identical types of models to what I've described, or if for some reason the problem I've described is generally very difficult and therefore no models exist, then I'd love to know also.</p>
  
Tags: <convolutional-neural-networks>
Title: CNN model to infer classes from unlabelled, unpartitioned data
UserDisplayName: Tom
ThreadText: # CNN model to infer classes from unlabelled, unpartitioned data
  
  (Asked by: username_0 on 2024-05-02)
  
  I'm wondering if there exists a CNN model (class of models?) designed to receive a single set of unlabelled, unpartitioned data, and attempt to infer classes. For instance, you could theoretically hand such a model a dataset containing some images of red squares and some images of blue circles, and the model would learn to distinguish the two types of images (perhaps by some kind of dimensionality reduction technique or Gaussian mixture model on the feature vectors).
  
  I feel like I've heard of similar techniques before, but my extensive Googling failed me. If there are similar but not identical types of models to what I've described, or if for some reason the problem I've described is generally very difficult and therefore no models exist, then I'd love to know also.
  
  **Tags:** convolutional-neural-networks
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2024-05-02. Score: 2 | Accepted answer
  
  what you want is a combination of dimensionality reduction technique and clustering.
  
  Indeed, you can't do naive clustering as the feature space is way too large, thus you need some sort of lower dimensional representation. To do so, you can even naively use an autoencoder, and use the latent representation as a lower dimensional feature space.
  
  On such manifold, you can then apply usual clustering techniques, such as DBScan, Kmeans and so on
  
  > **Comments:**
  > **username_0**: Thanks @Alberto! Do you know of any algorithms that currently exist like this? Designing something from scratch is a bit above my paygrade  
  > **username_1**: @Tom i don't have any specific end to end tool for this problem, sorry  
  
  ---
  
  