Questions tagged [unsupervised-learning]

Finding hidden (statistical) structure in unlabelled data, including clustering and feature extraction for dimensionality reduction.

454 questions
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Tips on preparing data for training on neural networks? [closed]

Still feeling a bit new to the world of neural networks. I am working with a CNN model right now (working with Keras), and would like to train it to identify certain types of objects from a dataset. I ...
42 views

k nearest neighbor classification algorithm

I'm currently studying about K nearest neighbour algorithm. I understand the basics of it. The problem I have is I have the below equation given in a slide and do not understand the purpose of it. ...
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Violating “compactness” in single linkage hierarchical agglomerative clustering

While I was studying Hierarchical Agglomerative Clustering in the book Elements of Statistical Learning in the chapter Unsupervised Learning, I came through the following : The statement The ...
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Deriving Multiplicative Update Rules for NMF

How to derive the multiplicative update rules for the non-negative matrix factorization problem given by Lee and Seung. Minimize $\left \| V - WH \right \|^2$ with respect to $W$ and $H$, subject ...
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Why does the Independent Component Analysis require non-gaussian?

This I found on google while I was going through the Independent Component Analysis in unsupervised learning. Let x = As where A is the Mixing Matrix. So, Lets assume that s here is Gaussian ...
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What is mean by the non-gaussianity in the independent component analysis(ICA)?

What is mean by non-gaussianity in ICA? Why do we use in ICA? How is Non-Gaussianity is an important and essential principle in ICA estimation? Following is the statement I found in a research paper....
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Unsupervised Outliers detection on time series

So I am looking ways to improve my current implementation of detecting outliers in work schedule. My data set is badge swipes for people. The current implementation finds outliers on in-times and out-...
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What is the precise definition of unsupervised learning?

Let's look at a special case: Generative Adversarial Networks (GANs). (For those who don't know what a GAN is: for this purpose they are two neural networks that are trained using user generated ...
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Determine the most important documents for supervised learning

I have somewhat of a general/high level question. Assume I'm doing supervised machine learning on some text data (tweets for example) and categorizing the documents to a certain taxonomy (multi-class ...
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Unsupervised Clustering of words from documents in 2 clusters

I'm new to these fields of Machine Learning, and I'll have to use unsupervised clustering of texts, that is make two clusters of words from a document, but without using the widely used K-Means ...
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Choosing the right model to learn [closed]

I'm new to the data science world, and I hope to solve a problem using deep learning methods, I started learning how FNN and CNN work and when I saw how many models and methods are the I got a bit ...
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Self-Organizing maps : is a N*M grid the same as a M*N grid? (with M different from N)

Self-Organizing maps : will a N*M grid give the same results as a M*N grid? (with M different from N)?
337 views

Assigning weights to variables to calculate rank/score of an Agent

I have data on Agents behavior history. I want to score each of these Agents based on the attributes. Attributes are both Categorical and Continuous.For this, I want to calculate the score by ...
244 views

Using Naive Bayes classifier for unsupervised learning

I was going through this article to learn about how the EM algorithm can be used to use the Naive Bayes algorithm for unsupervised learning. Suppose we have the following data without labels: 1 0 1 1 ...
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Optimal hidden units size

Suppose we have a standard autoencoder with three layers (i.e. L1 is the input layer, L3 the output layer with #input = #output = 100 and L2 is the hidden layer (50 units)). I know the interesting ...
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What are some applications of unsupervised HMMs?

Supervised HMMs can be applied to many problems like POS tagging and OCR (optical character recognition). I've learned that HMMs can be trained unsupervisedly using EM (Baum-Welch algorithm), what ...
535 views

What are supervised learning and unsupervised learning from a connectionist point of view

The general concept of supervised learning and unsupervised learning is very clear. In supervised learning, the decision on the unlabeled data is done after learning a classifier using available ...
502 views

Comparison between hierarchical clustering and principal component analysis (PCA)

I just read a article about the comparison between PCA and hierarchical clustering, but I cannot find the strengths and weakness of clustering compared Principal Component Analysis, what about other ...
387 views

How can unsupervised learning be performed by connectionist (neural network) algorithms?

Connectionist models of the mind (a subclass of which are neural networks) can be used to model a number of different behaviors, including language acquisition. They consist of a number of different ...
328 views

If regression is supervised learning is correlation unsupervised learning?

Regression is often given as a simple example for supervised learning because you have a dependent variable and try to build a model with the independent variables. Could you say that correlation is ...
27 views

NLP packages to check similarity between 2 sentences [closed]

Are there any NLP packages that can help to identify if two words or sentences are related to each other in some way? Like helmet is related to ...
25 views

Are hot machine learning solutions -like the Show, Attend and Tell paper- instances of unsupervised learning?

The Show, Attend and Tell paper describes a solution to image captioning. I am wondering: Is this novel machine learning application and instances of (semi-)supervised or unsupervised learning (in ...
111 views

Supervised vs unsupervised for anomaly detection

My problem is detecting the good/bad car reparations given the repair cost. And then I can rank the car garages based on the percentage of 'good' repair he got. A first simple model is using ...
293 views

Validating Clustering by Considering the Ratio of Intra-cluster to Inter-cluster Distance

I'm trying to evaluate a clustering method by looking at the ratio of the mean intra-clustering distance (the average distance between points in the same cluster) to the mean inter-cluster distance (...
672 views

GANS: Using Discriminator for prediction

In the past few years, GANs have been a hot topic and a lot of papers are being published every year regarding GANs. But I always see that either the results of the generator are being shown (sample ...
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Automatic fitting of normalization constant as a parameter in noise contrastive estimation

In the paper on Noise Contrastive Estimation, the authors define a parameterized density function $p_m^0\left(x;\alpha\right)$ to estimate the unnormalized PDF of the data, and then further define a ...
573 views

Unsupervised learning methods on unlabeled data?

I'm facing with a challenge of unsupervised classification of unlabeled data. The case is, I have circa 1.2 million vehicle warranty claims, and must develop a classification model to tell whether ...
This question could also (maybe) relate to PCA. I built a supervised RandomForest on a dataset that I'm currently working on - the actual V Prediction $R^2$ was holding around 80% across many CV ...