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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How would I encode a variable-length array to use an SVM?
I'm working on some image processing, and I have a list of contours
(it's essentially a list [or array] of (X, Y) coordinate pairs). These vary in length, depending on the size of the found contours.
...
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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 ...
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Is there a most general-purpose unsupervised learning algorithm?
I was thinking about training a model on non-linguistic material like video, and I was wondering if it could form concepts about the world, and also somehow form composite concepts or conceptual ...
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How to perform domain adaptation if there are only unlabelled data in both source and target domains
Recently I am reading literature regarding domain adaption. However, most of the works consider scenarios when there are some labelled data in the source domain. So I wonder if there is any ...
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How to identify patterns of errors in text recognition
I have a text recognition model that takes an image of a word as input and I want to identify if there are any specific patterns in the prediction that it consistently gets wrong.
I assume there must ...
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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. ...
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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 ...
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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) ...
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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-...
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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 ...
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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:
...
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Can kernel methods be used for prediction on unlabeled data?
In machine learning, kernel methods are often used in supervised learning, especially SVM. I would like to ask can kernel methods be used for prediction on unlabeled data under the premise of ...
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Unsupervised classification of objects based on relationships
I have size measurements of 1000 objects, measured over time. I would like to classify the objects based on the response of their size to time using unsupervised classification.
For example, the size ...
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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 ...
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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 ...
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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 ...
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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 ...
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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-...
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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 ...
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Which rule could I use to identify suppliers who are likely to leave us or stay with us?
I have 3 domains of supplier data (Jan 2017 to Jan 2022) and they are as follows
Purchase data - Contains all the purchase (of product) data made by the suppliers with us. It contains columns such ...
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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,...
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Unsupervised methodologies to detect collective anomalies in transaction data
I am researching various methodologies to detect collective anomalies in transactions data.
I have seen some supervised approaches, but not the unsupervised ones.
Please share any resources or ...
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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 ...
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Test accuracy decreases during my train process
I want to train a neural network model with the arcface loss function and try to combine it with domain adaption. But when the training process continues, I find the test accuracy first increases and ...
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Generating a dataset from data with "assumed" lables
I've got a task similar to the following:
Out of x amount of people, I need to predict, who could be a good athlete and who not. The thing is, I don't have data on the athletic performance of those ...
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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 ...
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What is the borderline between unsupervised learning and regular algorithms?
Unsupervised learning using neural networks is clearly machine learning since it is utilising neural nets.
However, some algorithms, k-means clustering, for example, are considered unsupervised ...
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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.
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Loss function to Push response value towards extremes
I have a feature map whose values are in the range of [0,1]. I want to push these values either towards extreme 0 or 1 using some loss function. Since I don't have any target value so it had to be in ...
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How does CURL extract labels from logits? [closed]
While going over the pseudocode of the CURL paper, the method to identify labels from the logits wasn't clear to me. I believe this technique might be common in other PyTorch/Deep Learning tasks. I ...
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How to learn transition type in a 1-hour extended DJ Mix?
How would you design a model which learns the transitions in a given 1-hour DJ Mix? To be specific, the model should be able to learn transitions, specify the occurring time and the type (Crossfade, ...
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What is the best clustering method to detect anomalies for data with mostly categorical data?
I have a dataset with about 85 columns. Out of the 85 columns, 70+ are categorical. My goal is to identify the outliers in this dataset through clustering methods as I do not have a target column.
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Can unsupervised models learn something from cat vocalizations?
I love cats, and over the years have noticed that they have recurrent patterns of vocalizations. For example, upon seeing a bird, a cat may start chittering, but the same cat would never chitter at ...
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Does Yann LeCun consider k-means self-supervised learning?
I was discussing the topic of self-supervised learning with a colleague. After a while we realized we were using different definitions. That's never helpful.
Both of us were introduced to self-...
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Is there a clustering algorithm that can make n clusters and the n+1 "others" cluster?
As far as I know all clustering algorithms assume that all delivered data points have to find its cluster.
My question is, is there an algorithm that could focus only on n clusters (number stated by ...
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What is the relation between self-taught learning and transfer learning?
I am new to transfer learning and I start by reading A Survey on Transfer Learning, and it stated the following:
according to different situations of labeled and unlabeled data in the source domain, ...
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Is there a bias-variance equivalent in unsupervised learning?
In supervised learning, bias, variance are pretty easy to calculate with labeled data. I was wondering if there's something equivalent in unsupervised learning, or like a way to estimate such things?
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user98235
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Which algorithm can be used for extracting text patterns in tabular data?
I am working with tabular data that is similar to the below:
Name
Phone Number
ISO3 Country
Amount
Email
...
...
Outcome
Possible Reason
Leona Sunfurry
(555)-555-5555
United States
58.96
leo_sun@...
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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 ...
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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 ...
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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 ...
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How does dimensionality reduction occur in Self organizing Map (SOM)?
We have n dimension input for SOM and the output 2-D clusters. How does it happen?
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What is the “Hello World” problem of Unsupervised Learning?
As a followup to this question, I'm interested in what the typical "Hello World" problem (first easy example problem) is for unsupervised learning.
A quick Google search didn't find any ...
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What should the output of a neural network that needs to classify in an unsupervised fashion XOR data be?
XOR data, without labels:
[[0,0],[0,1],[1,0],[1,1]]
I'm using this network for auto-classifying XOR data:
...
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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 ...
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Should forecasting with neural networks only be treated as a supervised learning (regression) problem?
I have recently made a work about the application of neural networks to time series forecasting, and I treated this as a supervised learning (regression) problem. I have come across the suggestion of ...
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Is this dataset with only two features suitable for clustering with k-means?
I am working with the K-means clustering algorithm for unsupervised learning.
Is the following dataset suitable for the k-means clustering task or not? Why or why not? The dataset has only two ...
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When labelled data is not available, what are some common unsupervised learning algorithms for pattern recognition that can be used?
In pattern recognition systems, when no labeled data is available, what are some common unsupervised learning algorithms for pattern recognition, that can be used?
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How can reinforcement learning be unsupervised learning if it uses deep learning?
I was watching a video in my online course where I'm learning about A.I. I am a very beginner in it.
At one point in the course, the instructor says that reinforcement learning (RL) needs a deep ...
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Applying Eligibility Traces to Q-Learning algorithm does not improve results (And might not function well)
I am trying to apply Eligibility Traces to a currently working Q-Learning algorithm.
The reference code for the Q-Learning algorithm was taken from this great blog ...
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