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
4
votes
1answer
177 views

What is major difference between different dimensionality reduction algorithms? [closed]

I find many algorithms are used for dimensionality reduction. The more commonly used ones (e.g. on this page ) are: ...
4
votes
1answer
462 views

Can a novelty detection model overfit?

Can a novelty detection model overfit? In novelty detection, the model is trained on normal data instances (not polluted by outliers) where no labels are used in the training process, while validated ...
5
votes
1answer
996 views

Combine two sets of clusters

I have two sets of topics obtained from two different sets of news paper articles. In other words, Cluster_1 = ${x_1, x_2, ..., x_n}$ includes the main topics of 'X' news paper set and Cluster_2 = ${...
0
votes
0answers
73 views

How can realize the evaluation/validation of unsupervised models through unlabeled data?

I'm researching anomaly detection, which is nothing else than outliers detection on a set of time-series web servers access log data or network traffic. Recently I re-faced to following fundamental ...
25
votes
1answer
31k views

Word2Vec vs. Sentence2Vec vs. Doc2Vec

I recently came across the terms Word2Vec, Sentence2Vec and Doc2Vec and kind of confused as I am new to vector semantics. Can someone please elaborate the differences in these methods in simple words. ...
7
votes
3answers
22k views

Anomaly detection on time series

I've just started working on an anomaly detection development in Python. My data sets are a collection of timeseries. More in details, data are coming from some sensors/meters which record and ...
3
votes
3answers
95 views

Better approach to assign values to determine potential fake sentences

I am trying to assign different values for each sentences based on information about the presence of hashtags, upper case letters/words (e.g. HATE) and some others. I created a data frame which ...
6
votes
1answer
2k views

What does it mean by “t-SNE retains the structure of the data”?

I was learning about t-SNE when I was told that t-SNE retains the structure of the data in the embeddings. What exactly does this mean ? How does the algorithm achieve this ? So far I have ...
5
votes
2answers
5k 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 ...
6
votes
2answers
8k views

Why will the accuracy of a highly unbalanced dataset reduce after oversampling?

I have created a synthetic dataset, with 20 samples in one class and 100 in the other, thus creating an imbalanced dataset. Now the accuracy of classification of the data before balancing is 80% while ...
3
votes
1answer
2k views

Estimating Predictive Uncertainty for unlabeled data

I am trying to estimate the predictive uncertainty for a deep neural network. While I do have a labeled training set, I´m trying to measure uncertainty for some unlabeled production data. This paper ...
5
votes
4answers
14k views

is it possible to do feature selection for unsupervised machine learning problems?

I started looking for ways to do feature selection in machine learning. By having a quick look at this post , I made the assumption that feature selection is only manageable for supervised learning ...
4
votes
5answers
7k 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 ...
2
votes
1answer
1k 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 ...
8
votes
1answer
757 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 ...
6
votes
1answer
324 views

How to compare two unsupervised anomaly detection algorithms on the same data-set?

I want to solve an anomaly detection problem on an unlabeled data-set. The only information about this problem is that the anomalies population is lower than 0.1%. It should be notice that the size of ...
5
votes
1answer
165 views

How do I interpret my result of clustering?

I am working on a clustering problem. I have 11 features. My complete data frame has 70-80% zeros. The data had outliers that I capped at 0.5 and 0.95 percentile. However, I tried k-means (python) on ...
3
votes
1answer
324 views

What value can I gain by doing exploratory data analysis on features (and thus data) before doing clustering?

This might not be a very good question, but I would still ask if it's beneficial to do EDA before running a clustering algorithm? I understand that EDA helps us generate good and helpful insights ...
2
votes
2answers
271 views

Document similarity

I have close to 50000 documents in plain text format. Is there a way in which I can group similar documents together? Similarity mostly here is the content similarity. Will transforming the text ...
2
votes
4answers
91 views

Unsupervised clustering without of Data which is supposed to be on a linear function

When I have a dataset where each datum has x and y, and the (x,y) has a relation of one of <...
1
vote
2answers
33 views

Unsupervised Clustering for n-length word arrays

I have a series of arrays [Apple,Banana,Cherry,Date] [Apple,Fig,Grape] [Banana,Cherry,Date,Elderberry] [Fig,Grape] and I would like to build some clusters that ...
0
votes
2answers
459 views

How to JUST represent words as embeddings by pretrained BERT?

I don't have enough data (i.e. I don't have enough texts) --- have only around 4k words in my dictionary. I need to compare given words, then I need to representate it as embedding. After the ...
0
votes
2answers
93 views

Theoretical treatment of unlabeled samples

In a typical supervised learning setting with a few positive and a few negative examples, it is clear that unlabeled data carries some information that can benefit learning and that is not captured in ...