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Questions tagged [unsupervised-learning]

For questions about AI that learns without being provided with a set of labels (expected answers) along with the set of input examples.

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0 votes
1 answer
234 views

Is there a way to select the subset of most important features using PCA?

Is there a way to select the most important features using PCA? I am not looking for the principal components with the highest scores but a subset of the original features.
1 vote
1 answer
122 views

Are there any advantages of using rules-based approaches versus models for detecting spam?

Suppose that we have unlabeled data. That is, all we have are a collection of emails and want to determine whether any of them is spam or not. Let's say we have $1,000$ rules to determine whether a ...
2 votes
2 answers
553 views

How can I use autoencoders to analyze patterns and classify them?

I generated a bunch of simulation data from a complex physical simulation that spits out patterns. I am trying to apply unsupervised learning to analyze the patterns and ideally classify them into ...
0 votes
0 answers
10 views

Linear dependency between features on unsupervised learning

I am preparing a numerical dataset to be trained using unsupervised learning methods (i.e. association with Apriori algorithm) in order to try to find instrinsic correlations hidden in the data. ...
0 votes
0 answers
12 views

How to knowing number of clusters when using SOM?

SOM uses neural network. The output layer of SOM should be neurons position. As the model is training, neuron's position started to moving to the closer of centroid of clusters. The output layer was ...
4 votes
1 answer
333 views

NEAT can't solve XOR completely

I'm currently implementing the NEAT algorithm. But problems occur when testing it with problems which don't have a linear solution(for example xor). My xor only produces 3 correct outputs once at a ...
0 votes
1 answer
120 views

In this example of fuzzy c-means, what is the difference between "sigma" and "center" for the clusters?

In this example, what exactly do "Cluster" and "Sigma" mean? (They chose random coordinates for the three centroids of the groups) Centers: Cluster centers, returned as a ...
0 votes
0 answers
19 views

Why are various deep learning models unsupervised? [duplicate]

While studying the field of deep learning, the questions I had from the beginning have still not been resolved. In general, supervised learning is known to solve problems by comparing the results ...
1 vote
2 answers
142 views

What technique is used for training Large Language Models like GPT?

I'm learning about GenAI, such as GPT (Generative Pretrained Transformer), and I'm particularly interested in understanding the training techniques used for these models. Deep learning, generally, can ...
0 votes
0 answers
29 views

Which main steps should I consider in order to successfully use a VAE for Anomaly Detection?

I am thinking about using the variational autoencoder model for anomaly detection . I have an Android Logs dataset. As the logs generated are a representative of time series type of data I thought ...
7 votes
1 answer
147 views

How does the network know which objects to track in the paper "Label-Free Supervision of Neural Networks with Physics and Domain Knowledge"?

I was reading the paper Label-Free Supervision of Neural Networks with Physics and Domain Knowledge, published at AAAI 2017, which won the best paper award. I understand the math and it makes sense. ...
16 votes
4 answers
10k views

What is the difference between self-supervised and unsupervised learning?

What is the difference between self-supervised and unsupervised learning? The terms logically overlap (and maybe self-supervised learning is a subset of unsupervised learning?), but I cannot pinpoint ...
0 votes
1 answer
72 views

How can I combine unsupervised learning with supervised learning?

I am currently using an isolation forest (from sklearn library) to detect anomalies in a data frame (basically it's a dynamic data frame more of a kind of time series I am. But I have certain criteria ...
1 vote
1 answer
125 views

Inquiry on Combining Two Neural Networks for unsupervised training: Has This Been Researched?

Hello AI Stack Exchange Community, I am exploring an idea related to neural networks, and I'm curious to know if this method has been previously researched or if there is a specific term for it. I am ...
0 votes
0 answers
24 views

I'm trying to build image search like Google Photo-Image with face is given to model & it'll get all the images in database in which he/she is present

When a user upload a selfie, the model search same person in dataset of images of multiple persons and get back all the images in which that person is present. Step 1: From dataset of images I detect ...
1 vote
1 answer
209 views

Reinforcement Learning vs Supervised Learning [duplicate]

I have never tried reinforcement learning in my life. I'm planning to apply it in robotics. I have some experiences using supervised learning mainly deep learning. So, that's mean I will use neural ...
0 votes
1 answer
22 views

Can we generate labels for an unlabelled dataset by doing some feature engineering?

I am very new to ML and currently, I am working on building a model that can predict recurring blood donors (a classification problem). I have a dataset which consists of 25 features (gender, height, ...
1 vote
1 answer
98 views

Aside from specific training sets, what distinguishes the capabilities of different AI implementations?

(Disclaimer: I don't know much about ML/AI, besides some basic ideas behind it all.) It seems like ML/AI models can often be boiled down to statistics, where certain levers (weights) get fine-tuned ...
0 votes
0 answers
74 views

Can pretraining be continued after RLHF?

Assume you have a pretrained transformer language model (M1) which already underwent reinforcement learning by human feedback (M2). I assume that it is in principle possible to continue the ...
0 votes
0 answers
29 views

Is it possible to combine SGD with an unsupervised learning approach effectively

Before I undertake quite a large project I would like to clarify whether my idea for training a multi-layer neural network will work. I plan to make an AI that can land a rocket from randomly ...
1 vote
3 answers
92 views

Do Artificial Neural Network with non-linear activation only in the output layer follows linearity?

I am using a model with linear activation in the hidden layer and non-linear activation in the output layer. Could you please help to know whether such models exhibit linearity or not? The non-linear ...
0 votes
0 answers
37 views

Is regression method the best for my case?

newbie here. I'm starting to work on a custom model for a very specific task, so I found no pre-trained models for this task so far. After checking (un)supervised learning approaches I believe that ...
0 votes
0 answers
11 views

How to compare word segmentation methods?

I am comparing a few methods of word segmentation in artificial language without dictionary and "golden" segmentation. Let's say, idolikecats is splitted ...
0 votes
1 answer
134 views

Can I implement a sklearn model inside a Pytorch nn.Module? [closed]

I am making a custom Pytorch model that at some point, clusters a latent space that was created by another, previous routine of the model (Autoencoder). In a bit more detail, my model is a regular ...
1 vote
2 answers
79 views

What can unsupervised learning actually be used for and how can humans interpret the outputs?

I am trying to refine my knowledge of AI, but unsupervised learning is a bit of a stumbling block for me. I understand that it finds 'hidden' patterns in data, but if they are hidden, how does a user ...
0 votes
0 answers
35 views

CNN without actuators

After training CNNs without actuators, I have an idea to compare their weights with each other using image mirroring. I am looking for ideas about reality perception of CNNs in this way. What might ...
0 votes
2 answers
125 views

How can I cluster based on the complementary categories?

K-means tries to find centroid and then clusters around the centroids. But what if we want to cluster based on the complement? For example, suppose we have a group of animals and we want to cluster ...
0 votes
1 answer
116 views

Unsupervised pretraining on the supervised learning training data

Is it ok to pre-train and train (fine-tune) the neural network on the same training data? Here is the specific context: I am using the TabNet model on a tabular dataset. The dataset is fully labeled. ...
0 votes
1 answer
50 views

Learning curve converges with huge errors

I am training an auto-encoder over $10^4$ epochs. I get a converging learning curve. However the error at the last stages stays huge $\sim10^{15}$. What does this mean? does it mean that my auto-...
1 vote
1 answer
71 views

Deep Clustering Approach for Unsupervised Video Anomaly Detection

I'm working on Unsupervised Video Anomaly Detection, and I've tried implementing the Generative Cooperative Learning method, with the help of this paper. The method uses a fixed backbone (ResNext-101) ...
0 votes
0 answers
7 views

Surveys, Papers, Hand on Tutorials about training data generation for anomaly detection

I am searching for anything related to supervised, semi supervised or unsupervised anomaly detection w.r.t training data generation. I am looking toward reading any work that tackles the issue how to ...
1 vote
1 answer
95 views

Survey on non-machine learning object detection algorithms

I am working on a project in which I will be performing object detection on deformed objects. Unfortunately, there isn't enough data sets to train them on some neural network. I am looking for ...
0 votes
0 answers
84 views

Autoencoder make spectrogram important parts more pronounced with a "log loss"

Hi I want to create a neural network that essentially picks out the most pronounced parts of a spectrogram. Assume this is the True spectrogram: ...
3 votes
1 answer
820 views

How can auto-encoders compute the reconstruction error for the new data?

Autoencoders are used for unsupervised anomaly detection by first learning the features of the data set with mainly "normal" data points. Then new data can be considered anomalous if the new ...
0 votes
2 answers
84 views

Which models can I use for supervised learning with images?

I have to do a project that detects fabric surface errors and I will use machine learning methods to deal with it. I have a dataset that includes around six thousand fabric surface images with the ...
3 votes
1 answer
718 views

Which Reinforcement Learning algorithms are efficient for episodic problems?

I have some episodic datasets extracted from a turn-based RTS game in which the current actions leading to the next state doesn’t determine the final solution/outcome of the episode. The learning is ...
4 votes
1 answer
345 views

How could an alien probe learn the basics of a language with only broadcasting signals?

While conducting research, I recently stumbled upon the deep learning and natural language processing concepts. In this question they say that the "grammar induction" is a "supervised ...
2 votes
1 answer
739 views

Should I use an unsupervised approach or train a classifier with many classes to build a deep image feature extractor?

I'd like to build a deep feature extractor of images (using a Bi-linear CNN). What would lead to the best results: an unsupervised approach (such as https://iopscience.iop.org/article/10.1088/1742-...
0 votes
1 answer
389 views

I have a 3 class classification problem. Detection of one of classes is very important. How to design the problem? one class classification or ...? [closed]

I have a 3 class classification problem. Correct detection of one of the classes is very important. How to design the problem: one class classification? a normal 3 class classification? two distinct ...
2 votes
1 answer
59 views

What clustering algorithms work best for datasets with only binary categorical features?

I have a dataset with a lot of binary categorical features and a single continuous target value. I would like to cluster them, but I am not quite sure what to use. In the past, I have used DBSCAN for ...
0 votes
1 answer
58 views

Is there a term for unquantifiably uncertain prior knowledge?

I'm working on a clustering algorithm which assigns each data point an index encoding its cluster. Index permutation is irrelevant to the correctness of the result. The algorithm is self-learning, in ...
0 votes
0 answers
21 views

Is there an unsupervised learning method for determine the most common questions within a dataset?

I have a dataset consisting of questions from customers. I am curious of the n most frequent asked questions, regardless of the variation the questions might appear in. Is there NLP methods for ...
4 votes
2 answers
88 views

Which metric should I use to assess the quality of the clusters?

I have a model that outputs a latent N-dimensional embedding for all data points, trained in a way that clusters data-points from the same class together, while being separated from other clusters ...
1 vote
1 answer
108 views

What happens if all the features are correlated with each other before clustering?

I know that when two features are highly correlated with each other, one of them should be removed from the dataset so they don't add twice the weight. However, what if all my features share a ...
2 votes
0 answers
27 views

What are the benefits of using spectral k-means over simple k-means?

I have understood why k-means can get stuck in local minima. Now, I am curious to know how the spectral k-means helps to avoid this local minima problem. According to this paper A tutorial on Spectral,...
2 votes
1 answer
76 views

Do Le et al. (2012) train all three autoencoder layers at a time, or just one?

Le et al. 2012 use a network of 1 billion parameters to learn neurons that respond to faces, cats, pedestrians, etc. without labels (unsupervised). Their network is built with three autoregressive ...
0 votes
0 answers
26 views

How to group multi-dimensional audio, video, and numerical data based on relatedness?

I have a data set that includes image arrays, point clouds, audio waveforms, and plain numerical data. I want to use unsupervised learning to group the data based on relatedness. So, if the audio and ...
3 votes
2 answers
82 views

What techniques to explore for dynamic clustering of documents (emails)?

I have a dataset of unlabelled emails that fall into distinct categories (around a dozen). I want to be able to classify them along with new ones to come in the future in a dynamic matter. I know that ...
4 votes
1 answer
738 views

How does an unsupervised learning model learn?

How does an unsupervised learning model learn, if it does not involve any target values?
3 votes
2 answers
184 views

Unsupervised learning to optimize a function of the input [closed]

I am looking to build a neural network that takes an input vector $\mathbf{x}$ and outputs a vector $\mathbf{y}$ such at $f(\mathbf{x}, \mathbf{y})$ is minimized, where $f$ is some function. The ...