Questions tagged [unsupervised-learning]
Finding hidden (statistical) structure in unlabelled data, including clustering and feature extraction for dimensionality reduction.
20
questions
3
votes
1answer
441 views
Unsupervised learning for anomaly detection
I've started working on an anomaly detection in Python.
My dataset is a time series one. The data is being collected by some sensors which record and collect data on semiconductor making machines.
...
5
votes
1answer
899 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
48 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
28k 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. ...
3
votes
3answers
84 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 ...
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 ...
5
votes
1answer
1k 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 ...
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
12k 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
6k 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
751 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
267 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
129 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
254 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
4answers
87 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 <...
2
votes
2answers
255 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 ...
1
vote
2answers
31 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
92 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 ...
