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Questions tagged [unsupervised-learning]

Unsupervised learning refers to machine learning contexts in which there is no prior 'training' period in which the learning agent is trained on objects of known type. As such, supervised learning includes such disciplines as mathematical clustering, whereby data is segmented into clusters based on the minimisation or maximisation of mathematical properties and not on an attempt to classify by understanding the right context.

Unsupervised learning (or clustering) refers to machine learning algorithms in which there is no 'label' available for the training data and the model tries to learn the underlying manifold. As such, unsupervised learning includes such disciplines as mathematical clustering, whereby data is segmented into clusters based on the minimization or maximization of mathematical properties and not on an attempt to classify by understanding the right context.

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Troubles in unsupervised domain adaptation with GCN

I am trying to implement an unsupervised domain adaptation network following the paper GCAN: Graph Convolutional Adversarial Network for Unsupervised Domain Adaptation, presented in CVPR in 2019 (can be found at this link). I have some trouble…
Neskelogth
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Unsupervised object detection

I am trying to detect the unique/foreign objects in a conveyor. The problem is in our case is, we don't know which type of featured object is passes through conveyor along with raw material. I am familiar with object detection techniques such as…
Berlin Benilo
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How to train a Keras model with very a big dataset?

I am trying to train an autoencoder using TensorFlow and Keras. My training data has more than 200K 512x128 unlabeled images. If I want to load the data in a matrix, its shape will be (200000, 512, 128, 3). That is a few hundred GB of RAM space. I…
Nirmal Baishnab
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Why are models such as BERT or GPT-3 considered unsupervised learning during pre-training when there is an output (label)

I am not very experienced with unsupervised learning, but my general understanding is that in unsupervised learning, the model learns without there being an output. However, during pre-training in models such as BERT or GPT-3, it seems to me that…
danielkim9
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k-medoids: control same agreement on class label

I've a problem with the control of the pattern of two class labels (1 and 2) results in the classification task using k-medoids. I'd like to apply the cluster::clara in two areas (ID) g2 and g3 and the same classification label for both areas, in my…
Leprechault
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How to get association rules with RHS more than length 1 in R

I am trying to understand the use of R to run the apriori algorithm to mine association rules.Referencing an online exam question with solutions provided on Studocs Q2.2, it is expected that I get 8 association rules which fulfill the minimum…
Prashin Jeevaganth
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Hierarchical clustering label based on their merging order in python

lets say, I have this type of Hierarchical clustering as below diagram. To get the clustering labels, I need to define proper threshold distance. For example, If I put the threshold at 0.32, I probably would get 3 clusters and if I set around 3.5, I…
user96564
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Why flatten last encoder layer in a convolutional VAE?

I am quite new in the deep learning game, I was wondering why do we flatten the last layer of the encoder in a VAE and then give the flattened output to a linear layer, which then approximates a location and scale parameter for the prior? Can't we…
Chris
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Dealing with class imbalance in unsupervised learning

I'm using K-mean clustering and I have no idea about the true labels of the data. I used PCA and I've got 4 clusters. However, the clusters seem to be imbalanced. I was wondering how I can fix the class imbalanced problem in this unsupervised…
Kimia Gharib
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How to use tf.Dataset in Keras model.fit without specifying targets?

I want to use an AutoEncoder model with Keras functional API. Also I want to use tf.data.Dataset as an input pipeline for the model. However, there is limitation that I can pass the dataset to the keras.model.fit only with tuple (inputs, targets)…
Tagir Akhmetshin
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deep neural network model stops learning after one epoch

I am training a unsupervised NN model and for some reason, after exactly one epoch (80 steps), model stops learning. ] Do you have any idea why it might happen and what should I do to prevent it? This is more info about my NN: I have a deep NN that…
DeepRazi
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Using Agglomerative Hierarchical Clustering on a high-dimensional dataset with categorical and continuous variables

My group and I are working on a high-dimensional dataset with a mix of categorical (binary and integer) and continuous variables. We are wondering what would be the best distance metric and linkage method to use for agglomerative hierarchical…
cornell_ML
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Is there K-Means clustering algorithm in R or Python that takes constraints as inputs?

I'm working on a 2-dimensional dataset for segmentation (inputs are just latitudes and longitudes) and I want to use K-means but at the same time to be able to specify minimum and maximum number of units in clusters, as well as the maximum distance…
Scratch
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K-means Clustering: How to determine which variables influence a cluster?

I am performing a cluster analysis on 86 different variables, which I managed to reduce to 19 PCs using PCA. Using sk-learn's K-means clustering algorithm, I got 10 clusters. However, I can't figure out which variables are responsible for…
Ahmed Jyad
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Expectation Maximization Algorithm (EM) for Gaussian Mixture Models (GMMs)

I'm trying to apply the Expectation Maximization Algorithm (EM) to a Gaussian Mixture Model (GMM) using Python and NumPy. The PDF document I am basing my implementation on can be found here. Below are the equations: When applying the algorithm I…
Blg Khalil
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