Questions tagged [unsupervised-learning]

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

Filter by
Sorted by
Tagged with
1
vote
1answer
586 views

In it necessary to split train, test, validation dataset for unsupervised machine learning algorithm (eg. autoencoder)?

Generally in supervised machine learning algorithms, the model performance is measured splitting train, test, validation set. But in case of unsupervised method , like autoencoder, is it necessary to ...
1
vote
0answers
145 views

Spectral Clustering using Negative Euclidean Distances

In most spectral clustering papers I've seen (von Luxburg's tutorial, Michael Jordan's NIPS paper, and some papers that predate those), they like using the affinity matrix generated by the Gaussian ...
1
vote
1answer
667 views

Unsupervised learning examples in Matlab

I am trying to classify ECG data into abnormal and normal using unsupervised learning methods in Matlab. The problem is that whilst I am used to supervised learning algorithms, I have never seen how ...
2
votes
0answers
84 views

Clustering data sitting close to corners of an N-dimensional parallelepiped

I am looking for a method of clustering data that are close to the corners of an $N$-dimensional parallelepiped (but I don't know the vectors spanning it). Is there a good method for finding ...
1
vote
0answers
116 views

Classifying mouse gesture patterns based on time series of mouse coordinates

Hint: I am a programmer, but not a machine-learning guy, so be patient with me ;) I am currently working on a side project for finding patterns in mouse movement data. Example of patterns I am ...
0
votes
0answers
64 views

Reinforcement learning with 'actions' and unlabelled data

I am interested in researching a machine-learning algorithm for trading on Forex, after being inspired by several papers that I read. I want to do this more out of a love of computing and forex than ...
2
votes
2answers
159 views

Where do artificial neural networks belong in the 'taxonomy' of statistical learning methods?

I'm a non-stats person trying to learn more about statistical learning methods, and to organize my thinking I am trying to construct a mental taxonomy of the methods I'm learning about. For instance: ...
0
votes
1answer
137 views

Oja's rule gives unit eigenvector

Does Oja's rule for normalized Hebbian learning always result in a unit eigenvector which corresponds to the largest eigenvalue? Or are there any specific conditions or assumptions under which this is ...
3
votes
5answers
626 views

An algorithm similar to (or based on) K-means that do not require the 'k' number of clusters

These days I'm using a lot (and discovering) nice ways to use k-means' clustering. For example, clustering word embeddings (word2vec vectors) to find synonyms or clustering doc vectors (doc2vec) to ...
1
vote
1answer
49 views

Extracting common sequences from time sequence data

I have a large number of time ordered location traces that I'd like to extract common sequences from. These locations are mapped from latitude, longitude pairs to a 2D aggregation bucket to handle ...
0
votes
0answers
22 views

Feed guess features into unsupervised learning classification?

I've got an completely unlabeled dataset and my task is to classify it into positive and negative, two categories. As the data is unlabeled, I have to choose unsupervised classification; however, we ...
1
vote
1answer
143 views

Binary Sparse Coding

In this binary sparse coding paper referenced in the Goodfellow/Bengio/Courville deep learning book (https://fias.uni-frankfurt.de/~bornschein/papers/HennigesEtAl_lva2010.pdf), the parameter $\pi=p(...
2
votes
0answers
34 views

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 ...
1
vote
1answer
583 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 ...
6
votes
4answers
688 views

t-SNE dimensions as additional predictor variables

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 ...
2
votes
0answers
72 views

Visualizing neural network inferences

I know this is an ongoing and hard question to answer, but if anyone has experience in this then please share your knowledge. Suppose I have made a neural network with the task of predicting an ...
0
votes
1answer
382 views

One-class SVM: “training set vs. origin” logic

first of all, I apologize about asking this question again since a similar one was posted recently; I had to repost it since I still don't understand the answer and I had no other way to interact with ...
0
votes
1answer
92 views

Quantify similarity between self-organizing maps (SOMs)

What would be a valid similarity measure to quantify the (dis)similarity between two different datasets processed using the same trained version of a self-organizing map (trained on the combination of ...
2
votes
0answers
131 views

Random forest - extracting profiles

I'm using the good old decision tree (CRT or CHAID algorithm, depending on the situation) in order to predict voting behavior and extract some profile (e.g: Women who live in the suburbs, who are not ...
0
votes
0answers
68 views

What is a medium-sized dataset for scikit learn clustering algorithms?

On scikit learn's clusting algorithm page they give a comparison chart for different clustering algorithms describing their scalability and usecases. What is considered a medium sized n_samples? I ...
0
votes
3answers
433 views

Prediction after PCA and K-Means

I have a data set with a large amount of features. I'm applying PCA on it in order to run it through K-means, to discover clusters in my data set. I'd like to know what is the best practice to make ...
3
votes
1answer
2k views

Unsupervised training of CNN

I have some unlabeled 1D (i.e. time-domain) signals (real neuron measurements) that I would like to classify in 3 classes. I would like to use a ConvNet to do this. However, as far as I know, ConvNets ...
1
vote
1answer
248 views

Supervised, semi-supervised, or unsupervised? Confused

BACKGROUND: Consider the problem in cybersecruity that consists of classifying domain names as either malicious or legitimate based on various features such as the URL string, the name of the ...
2
votes
1answer
134 views

what are the top level subsets/domains of ML?

I'm not really happy with the mind maps I've been able to find on Google, most of them are algorithm based. I want to make a good one that is problem/solution domain based. Do I have this right for ...
2
votes
1answer
375 views

Clustering before regression

Could there be any benefit to running a clustering algorithm on a data set before performing regression? I'm thinking that it might be useful to run a regression algorithm on each cluster thereby only ...
2
votes
0answers
270 views

State-of-the-art: unsupervised learning for patterns in text

My gut tells me tried-and-true approaches like k-means and Latent Dirichlet Allocation may no longer be state-of-the-art approaches for unsupervised learning with text data, what with models like ...
1
vote
0answers
140 views

Prediction using Hidden Markov Models

I am trying to model financial time series data using Hidden Markov Models. This question is related to time series analysis in general. Can I create the model on previous few days data and use that ...
1
vote
0answers
58 views

How does using a correlated topic model effect the distribution of topics?

Correlated Topic Models are a great advance on the original topic model - see Blei and Lafferty 2007 for more info. My question is this - how does a Correlated Topic Model impact the overall ...
0
votes
0answers
755 views

Learning normal distribution with VAE

I am trying to use a Variational Autoencoder to learn a multivariate normal distribution. I know that from practical point of view this is pointless, as we can sample from a normal distribution itself,...
3
votes
1answer
411 views

Which methods can help us to understand clustering model is good or bad?

In some clustering algorithm, ex: K-Means cluster, it is very sensitive with outliers, so we need to remove outliers before aplly ...
2
votes
0answers
312 views

Clustering of high dimensional data

I am having a data set with 54 independent variables .Most of them are having zeros it resembles like sparse matrix .How to cluster this kind of data and is there any data pre processing like Box cox ...
1
vote
0answers
178 views

Similar to Self Organization Maps, are all other clustering algorithms “self organizing”?

From my understanding of the terms: Self means: No supervision is required during training. Organizing means: To create a topographic ordered map by using unsupervised competitive learning and ...
3
votes
1answer
158 views

Does online learning theory have any real world applications?

This is a question regarding the specific application of online learning theory in the sense of http://www.mit.edu/~9.520/spring08/Classes/online_learning_2008.pdf I went through ICML papers for 2017 ...
2
votes
0answers
300 views

How to do model selection in the unsupervised learning setting?

For supervised learning, we know the correct answers for samples, model selection is more easier, we can use k-ford cross validation (this site!) and etc. But for unsupervised learning, e.g. ...
3
votes
0answers
198 views

Topological data analysis and evaluating dimensionality reduction

I did an exploration some time ago on using TDA tools to see how topological features change after application of some nonlinear dimensionality reduction methods. For example I found out that, for ...
0
votes
0answers
51 views

How can I use the results of clustering algorithms for classification

I'm doing a mobile customer segmentation and I was using K-means to cluster my data according to the various data points (location, time of use, duration used for etc). After reading a lot of posts in ...
0
votes
1answer
441 views

How to validate k-means result [duplicate]

I'm doing anomaly detection on unsupervised data using k-means I got a result but I don't know how to validate my clustering result. by plotting I can see my anomalies but how should I validate that ...
0
votes
1answer
256 views

Threshold for kmeans anomaly detection

I'm learning the kmeans to find out anomaly from the dataset. but I don't know how to set threshold. I tried by the putting mean of the centroid to point distance but it's not working, half my record ...
0
votes
1answer
3k views

how to handle sparse data problem in unsupervised learning .i'm going to use k means on data set

how to handle sparse data problem in unsupervised learning .i'm going to use k-means on the dataset. I have 200 variables, nearly in each column have 70% zeros. how can I handle without discarding any ...
0
votes
1answer
46 views

How to improve classification algorithms when the features are unlabeled?

hey guys I'm dealing with data that has around 15 features that are all positive, real numbers but I have no idea what the features are. The headers of my data are all just "feature1", "feature2", "...
0
votes
1answer
275 views

What is a good reason why reinforcement learning relies on Q value instead of the reward r?

I am new to reinforcement learning and I am reading off of some tutorial materials, but I have noticed a seldom discussed assumption that we should calculate our action based on Q-value instead of the ...
0
votes
0answers
60 views

How can I classify my text by using unsupervised approaches?

I have a corpus of newspaper articles and would like to classify them according to content (words, phrases) that I determine beforehand. The only way to do this that I found so far is by looking at ...
0
votes
1answer
56 views

Learning vs. training

In my head, these two words (learning and training) seem to somehow have a fuzzy boundary between them. For example, the word learning for me conveys the idea of training; if I want to learn ...
1
vote
0answers
33 views

How can we learn the values of the parameters for Levenshtein distance?

I am trying to filter out similar-looking names from a database. Once I have figured out the names, I will merge them into a single entity. To achieve this, I am planning on using edit-distance ...
1
vote
1answer
2k views

how to handle missing data in clustering problem

The features in data sometimes contains missing values, which mean the value is unknown. If I replace unknown value with a special normal value like "0", then the clustering algorithms will trade them ...
0
votes
1answer
35 views

What is the best way to analyze and predict based on a dataset that has both text and numbers?

I have a Twitter dataset that has both the tweets themselves, but also metadata about the user as number of tweets, date of creation, and other numerical values. How can I make predictions based on ...
2
votes
0answers
54 views

How can knowing test data improve the performance of face recognition?

I don't think knowing face recognition is necessary to answer this question, any suggestions are welcome. For face verification problems, usually images are first converted into fixed length vectors ...
1
vote
1answer
35 views

Improving clusters

Say we have 10 classes. We have obtained two kinds of clusters in an unsupervised manner. One type is heavily clustered, i.e, say it shows only 4 clusters and one heavily cut, i.e, it shows say, 20 ...
5
votes
5answers
500 views

Choosing a model for my unsupervised machine learning problem

I need to choose a model for unsupervised machine learning problem. There are 4 clusters in 3D space. These are my requirements: I will run the same model multiple times with different training data (...
0
votes
1answer
154 views

Resulting clusters are very uneven

I am clustering a set of 50k products. I would expect the resulting clusters to be things like "organic chicken", "orange juice", etc. I am using the bag of words model, and there are about 8k ...