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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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Do Expectation Maximization always have to converge after a finite number of iterations if the implementation is correct?

I'm trying to understand how this could be possible, I know EM algorithm has the property to increase the likelihood for each step. However, this does not imply convergence. My question is, if the implementation IS proper and correct, EM will always…
tu_1329
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ValueError: Setting a random_state has no effect since shuffle is False. You should leave random_state to its default (None)

Whilst working on an anomaly detection problem on PyCaret, I ran into this issue. I couldn't find that many resources online, unfortunately: from pycaret.datasets import get_data dataset = get_data("mice") data_train = dataset.sample(frac=0.95,…
Muhammad Bello
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How to predict on new data with saved OPTICS clustering model

I work with density based clustering and usually cluster on data (text) as and when I get it. However, I want to save and re-use one of my clustering models since it would reduce memory costs by sending my data sequentially instead of having to dump…
Aamir Syed
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Batch 512 in TFRecordDataset does not work on Unsupervised model

I'm using Mnist dataset for testing the Unsupervised model. The dataset and output: test_dataset = tf.data.TFRecordDataset([test_filenames]) test_dataset = test_dataset.map(map_func) test_dataset =…
stackbiz
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Clustering ranking

I'm analyzing a data in R where predictor variables are available but there is no response variable. Using unsupervised learning (k-means) I have identified patterns in the data. But I need to rank the clusters according to their overall performance…
lingezh
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Clustering small image set with unknown number of clusters

I am working on a project where I am trying to cluster different small image sets (each set has around 100 images) without knowing how many classes can each set contain (it can go from 2 to 4). I have tried using a convolutional autoencoder for…
mohamed karaa
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Spatial Points Outlier Clustering Method

I would like to implement an unsupervised clustering to detect grids (vertical/horizontal lines) for spatial points. I have tried DBSCAN and it gives subpar results. It is able to pick out the grids as seen in red below: However, it is not able to…
eun ji
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Project training data onto PCA R

I'm a total beginner in ML, R, you name it and I'm using FactoMineR's PCA function on my training set to find the principal components of my data. res_pca <- PCA(training, scale.unit=TRUE, graph=FALSE) Now I have to project my training data onto…
IDK
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Clustering text data based on sentiment?

I am scraping reviews off Amazon with the intent to perform sentiment analysis to classify them into positive, negative and neutral. Now the data I would get would be text and unlabeled. My approach to this problem would be as following:- 1.) Label…
Gee
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Difference between Graph Neural Networks and GraphSage

What is the difference between the basic Graph Convolutional Neural Networks and GraphSage? Which of the methods is more suited to unsupervised learning and in that case how is the loss function defined? Please share the base papers for both the…
Omar Rafique
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Time Series Segmentation using Python

I have a TimeSeries dataset which has plots like the one showed below. I am trying to find the best way to do segmentation of the time series. I need the time series divided into three regions - 'RampUp', 'Plateua' and 'CoolDown' for the initial…
Amruth Kiran
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A numeric version of a sckit-learn tf-idf word-frequency array?

I'm trying to learn more about unsupervised learning in Python. When I was doing a couple of courses on DataCamp, I noticed that scikit-learn had feature extraction but it was only for text. However, what if the list I have is all numbers. Is there…
Steve Brother
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Creating a hybrid clustering method with K-Means & Agglomerative Clustering

I'm trying to create a clustering method that combines K-Means and Agglomerative Clustering. The first step would be to apply the K-Means algorithm to group the data into 50 clusters. From the centroids and labels obtained for each cluster, Second,…
yahia bouzoubaa
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tensor flow with untrainable layer

I am looking at this error. I am making a part of the model untrainable, I get an error in the .fit. would like to freeze the start of the model to only trade the head. top is the model, bottom is the .fit def snakeEyes(): K = 100 i …
Mika S
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To what corresponds 2 and 3 in ResNet-152 (3×) and ResNet-152 (2×)

I use Resnet-50 and ResNet-152 to implement my code, I read this: We also note that ResNet-152 (3×+SK) is only marginally better than ResNet-152 (2×+SK), So, I would like to undrestand the main difference between ResNet-152 (3×) and ResNet-152…
Eliza
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