Questions tagged [unsupervised-learning]

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

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1answer
148 views

Probability for label correctness in semi-supervised learning

I am aware of the existence of semi-supervised learning approaches, such as the Ladder Network, where only a subset of the data is labeled. Are there any methods or papers which consider correctness ...
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1answer
4k views

Why my loss is negative while training SAE?

I am using loss='binary_crossentropy' here is my code: I tried to increase number of training image and Epoch ,but that did not help me. ...
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2answers
4k views

Multidimensional Dynamic Time Warping Implementation in Python - confirm?

I believe that I implemented MDTW in python here but I don't know if I did it correctly. The results seem intuitive. Can someone look at this code and tell me if you see anything wrong? A lot of the ...
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2answers
330 views

Scalar entities for k means clustering

I am trying to understand kmeans clustering and I read a article where kmeans is used for clustering the features generated in network logs. This clustering is followed by a supervised classification. ...
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3answers
853 views

Which outlier detection can detect these outliers?

I have a vector and want to detect outliers in it. The following figure shows the distribution of the vector. Red points are outliers. Blue points are normal points. Yellow points are also normal. ...
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1answer
73 views

unsupervised learning in medical systems and intelligent systems?

I have a dataset which belongs to a hospital. It contains data about patients and healthy people. The problem is separating healthy ones from patients. I add some new features to dataset to solve this ...
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0answers
79 views

Hidden Markov Models: Linking states to labels after EM training

The tl;dr version first: I have the following problem: I implemented Baum Welch for ergodic HMMs. I do it like this: I pass the model two number C1 and ...
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0answers
1k views

SOM initial values for learning rate and neighborhood sigma

I am using SOM (Self-Organizing Maps) of Kohonen, or more specifically, the MiniSom, found here to cluster and visualize my data. As you can see in the above site, the example given is: ...
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2answers
10k views

Does it make sense to train a CNN as an autoencoder?

I work with analyzing EEG data, which will eventually need to be classified. However, obtaining labels for the recordings is somewhat expensive, which has led me to consider unsupervised approaches, ...
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2answers
3k views

Is Overfitting a problem in Unsupervised learning?

I come to this question as I read the use of PCA to reduce overfitting is a bad practice. That is because PCA does not consider labels/output classes and so Regularization is always preferred. That ...
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3answers
3k views

How to use GAN for unsupervised feature extraction from images?

I have understood how GAN works while two networks (generative and discriminative) compete with each other. I have built a DCGAN (GAN with convolutional discriminator and de-convolutional generator) ...
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0answers
50 views

Looking for an algo transforming numerical attributes into categorical attributes -cleverly

I created an algorithm which works on categorical attributes. The input data comes with categorical attributes, but numerical ones as well. How can I apply a pre-processing which transforms the ...
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0answers
343 views

Examples for predict.FAMD?

I am doing a study on unsupervised data with various categorical variables. So I have found the FactoMineR package to be really handy for this, particularly with the FAMD functions. I can get to a ...
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0answers
504 views

HMM - Matlab for data set to detect anomaly

I have a dataset of oil temperatures. The time series consist of 100 hours of measurement at every second. There is an anomaly in the data that I would like to detect using Hidden Markov Models (HMM). ...
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1answer
52k views

Confused about how to apply KMeans on my a dataset with features extracted

I am trying to apply a basic use of the scikitlearn KMeans Clustering package, to create different clusters that I could use to identify a certain activity. For example, in my dataset below, I have ...
2
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1answer
223 views

What is the difference between Slow Feature Analysis (SFA) and a Moving Average?

I have started to read more about Slow Feature Analysis and I was wondering how SFA differed from simply taking a moving average? The linked article suggests, "SFA is an unsupervised algorithm that ...
8
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2answers
9k views

Clustering high dimensional data

TL;DR: Given a big image dataset (around 36 GiB of raw pixels) of unlabeled data, how can I cluster the images (based on the pixel values) without knowing the number of clusters ...
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2answers
178 views

Using a K-NN Classification Approach for Time Series Data?

I have a dataset which contains time-series data of water flow over time. I have a flow meter connected to a kitchen faucet, and I am trying to cluster or classify specific water usage events. The ...
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0answers
179 views

Global vs. local bias-variance tradeoff

In the standard example of decomposing the MSE into Bias, Variance and Irreducible error: $$MSE(x) = \left(\mathbb{E}[\hat{f}(x)] - f(x) \right)^2 + \mathbb{E}\left[\left(\hat{f}(x) - f(x)\right)^2\...
2
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1answer
958 views

How hidden layer is made binary in Restricted Boltzmann Machine (RBM)?

In RBM, in the positive phase for updating the hidden layer(which should also be binary), [Acually consider a node of h1 ∈ H(hidden layer vector)] to make h1 a binary number we compute the probability ...
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1answer
320 views

# of iterations in Restricted Boltzmann Machine (RBM)

I have a training set, I provide it (consider a data from training set) to the visible layer. Then the normal process is followed, i.e. Positive Phase-> Negative Phase-> Reconstruction of weights, ...
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3answers
2k views

Intuition Behind Restricted Boltzmann Machine (RBM)

I went through Geoff Hinton's Neural Networks course on Coursera and also through introduction to restricted boltzmann machines, still I didn't understand the intuition behind RBMs. Why do we need ...
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2answers
4k views

What methods can be used to detect anomalies in temporal texual data?

I've been looking for methods that can help figure out anomalies in textual data stored in databases. Major goal is to use a unsupervised learning method to detect the anomalies. Further how can I ...
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0answers
76 views

Neural Network: how to utilize weakly-/unsupervised data to improve supervised network?

Let's consider one has built a fully-supervised neural network for some task, e.g. localizing an object in various scenes. As you can imagine, it is quite time-consuming to label data: one has to ...
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0answers
2k views

Grid Search on Unsupervised Sklearn Clustering?

I am trying to use clustering algorithms in sklearn and am using Silhouette score with cosine similarity as a metric to compare different algorithms. My question is due to the varying hyperparameters ...
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2answers
669 views

Unsupervised Classification for documents

I'm trying to create a classifier in which there is less "manual" work for the user. For less manual work I mean that there won't be an initial phase of manual labeling of a training set, like in ...
2
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1answer
1k views

Online Variational Autoencoder

when training a VAE, typically one samples from the latent distribution using the reparametrization trick using a fairly large minibatch size (>100) in the decoder/generator half of the VAE. I'm ...
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1answer
4k views

Cluster tendency using Hopkins statistic implementation in Python

The Hopkins statistic, is a statistic which gives a value which indicates the cluster tendency, in other words: how well the data can be clustered. If the value is between {0.01, ...,0.3}, the data ...
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0answers
47 views

Clustering objects defined by vector

I have a set of objects (98 total). I need to cluster these objects based on their pairwise distance. Each pair contains approximately 2000 values (not consistent). This is what I have done so far. ...
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2answers
63 views

Clusters with bounded diamter

In my application, I want to have clusters whose diameters are bounded by some fixed number. Also, the number of clusters in the data is unknown and therefore the clusters must be discovered without a ...
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1answer
297 views

How to create a social network like IBM's Watson News Explorer?

I was much impressed by the news-explorer view of IBM.It shows a network view with on searching a keyword and it explores the relational edges with a graph. Here it is I think it uses the k-means ...
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2answers
2k views

generalized likelihood ratio test (GLRT)

I am having trouble in understanding the generalized likelihood ratio test (GLRT). Can anyone explain what it is to me, or point me toward an easy-to-understand reference? Is it a supervised or ...
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0answers
29 views

With EM algorithm, can you infer the location and variance of each “peak” in a pdf? Gaussian Mixture Models?

When I plot my data into bins, there is a frequency of data points per bin, which I can plot with a histogram. Based on this probability density function, I would like to find the maximum likelihood ...
2
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1answer
183 views

statistics or robust statistics for identifying multivariate outliers

For the single variate data sets, we can use some straightforward methods, such as box plot or [5%, 95%] quantile to identify outliers. For multivariate data sets, are there any statistics that can be ...
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2answers
153 views

News clustering on unlabeled datasets

I currently have a bunch of extracted news articles to perform news classification. However, the articles are unlabeled. There are about 160k articles therefore manually labeling them is impossible. I'...
3
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1answer
527 views

Unsupervised binning of non-normal data

For some $8000$ customer profiles, in addition to a data-set, I have two kinds of scores available: Type 1 Score ranges from $0$ to $1$ and gives the prediction ...
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5answers
4k views

Best approach for this unsupervised clustering problem with categorical data?

I'm a software engineer new to Machine Learning. I've read about basic non-supervised techniques like k-means and hierarchical clustering and now I'm trying to put them into practice with a basic ...
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1answer
97 views

Is what I did supervised or unsupervised Machine learning?

My goal is to get a smartphone names from Twitter. So this is what I followed: 1- I extracted 100K tweets using the keyword “smartphone”. 2- I Applied LDA after applying ngram tokenization and ...
0
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1answer
250 views

Clustering based on partial information?

I'm open to suggestions on how to improve the title. My problem is this, but I think it's a more general problem. In my context, I have a lot of data which has location data (Lat/Lon) as well as ...
2
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3answers
1k views

Machine Learning with sometimes missing data

I'm trying to do an indoor locationing system based on my RSSI signal on my routers, I'm sniffing my network so I know what's the RSSI of my phone related to my routers antennas (I have 5 antennas all ...
8
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1answer
721 views

Ideas for prospect scoring model

I have to think about a model to identify prospects (companies) that have a high chance of being converted into clients, and I'm looking for advice on what kind of model could be of use. The ...
5
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3answers
779 views

Classification technique for unsupervised data?

I have unsupervised data (i.e this data doesn't have any target variable through which I can learn it's prior behaviour) it is a mix of continuous and categorical data. Now I want to classify the test ...
4
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2answers
2k views

Constrained k-means algorithms in R (must-link constraints)

I currently face an unsupervised learning task that is to be approaches using clustering. More specifically, it is a segementation task and hence there is some prior knowledge about a) the number of ...
3
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0answers
310 views

sLDA vs. LDA+Classifier

For simplicity, suppose we're looking at Yelp reviews of restaurants, and are trying to classify the restaurant by cuisine type (e.g. "Italian, Japanese," etc.). Lets also assume our data already a ...
5
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4answers
403 views

Categorical Clustering of Users Reading Habits

I have a data set with a set of users and a history of documents they have read, all the documents have metadata attributes (think topic, country, author) associated with them. I want to cluster the ...
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2answers
46 views

Identifying baseline consumption

I have data of intraday electricity consumptions (by half hours - 48 a day) over a year of 4000 households. Task is to establish baseline consumption of each of these households - possibly also ...
2
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0answers
85 views

Finding dominating attributes with in the clusters generated

I am having a dataset of customers where each customer is represented as some feature vector and I am applying K-means algorithm to this dataset. On the basis of those features, I can abstract and ...
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1answer
154 views

Best network structure for unsupervised learning [closed]

Currently using networkX to build a probability based network for unsupervised learning, it is a basic reader understanding relational connectivity through probabilities, network slows significantly ...
5
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1answer
139 views

Inferring Relational Hierarchies of Words

I am new to natural language processing and I have not heard of a problem similar to mine yet. I was wondering if anyone could refer me to a method for solving my problem, or tell me how this problem ...
2
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1answer
99 views

What to do with stale centroids in K-means

When I run Kmeans on my dataset, I notice that some centroids become stale in the they are no longer the closest centroid to any point after some iteration. Right now I am skipping these stale ...

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