Frequent 'unsupervised-learning' Questions

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: ...

unsupervised-learning dimensionality-reduction

asked Aug 16 '20 at 10:49

rnso

1,3562 gold badges13 silver badges30 bronze badges

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 ...

asked Sep 27 '20 at 14:12

s_am

535 bronze badges

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 = ${...

machine-learning data-mining unsupervised-learning topic-model dirichlet

asked Aug 17 '17 at 1:32

Volka

6512 gold badges6 silver badges19 bronze badges

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 ...

deep-learning unsupervised-learning anomaly-detection evaluation validation

asked Dec 3 '20 at 14:38

Mario

3572 silver badges13 bronze badges

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. ...

machine-learning data-mining clustering nlp unsupervised-learning

asked Jun 30 '17 at 7:05

Smith

4791 gold badge5 silver badges14 bronze badges

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 ...

machine-learning python time-series unsupervised-learning anomaly-detection

asked Aug 28 '18 at 16:34

Giordano

2951 gold badge4 silver badges9 bronze badges

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 ...

machine-learning python decision-trees text-mining unsupervised-learning

asked Jun 3 '20 at 9:49

Math

1519 bronze badges

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 ...

machine-learning visualization unsupervised-learning dimensionality-reduction

asked Aug 13 '18 at 18:18

Tanmay Bhatnagar

2112 silver badges4 bronze badges

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 ...

machine-learning unsupervised-learning pca overfitting

asked Mar 16 '17 at 14:44

Ronak Agrawal

1972 silver badges11 bronze badges

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 ...

classification dataset unsupervised-learning supervised-learning class-imbalance

asked Feb 23 '18 at 8:51

girl101

1,1131 gold badge11 silver badges19 bronze badges

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 ...

neural-network deep-learning unsupervised-learning prediction

asked Apr 4 '18 at 11:18

L. Anders

314 bronze badges

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 ...

feature-selection unsupervised-learning

asked Mar 27 '18 at 9:27

ChiPlusPlus

4951 gold badge5 silver badges13 bronze badges

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 ...

machine-learning clustering categorical-data unsupervised-learning

asked Jul 20 '16 at 15:51

user1813867

1411 silver badge2 bronze badges

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 ...

machine-learning neural-network deep-learning unsupervised-learning rbm

asked Dec 10 '16 at 2:56

Born2Code

3371 silver badge10 bronze badges

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 ...

machine-learning predictive-modeling supervised-learning unsupervised-learning

asked Jun 15 '16 at 11:30

François M.

2151 silver badge6 bronze badges

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 ...

unsupervised-learning anomaly-detection class-imbalance evaluation

asked Mar 20 '19 at 9:52

Alireza Zolanvari

6363 silver badges19 bronze badges

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 ...

data-mining clustering unsupervised-learning k-means pca

asked Jan 24 '20 at 20:17

Akash Dubey

7141 gold badge5 silver badges12 bronze badges

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 ...

machine-learning data-mining clustering unsupervised-learning k-means

asked Jan 22 '20 at 22:14

Akash Dubey

7141 gold badge5 silver badges12 bronze badges

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 ...

machine-learning nlp unsupervised-learning supervised-learning similar-documents

asked Jan 24 '20 at 10:14

praneeth

494 bronze badges

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 <...

clustering unsupervised-learning

asked Mar 5 '18 at 2:13

kensuke1984

1232 bronze badges

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 ...

unsupervised-learning

asked Sep 22 '19 at 22:43

Jamie Dixon

1354 bronze badges

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 ...

nlp unsupervised-learning word-embeddings bert representation

asked Apr 13 at 22:03

taciturno

1376 bronze badges

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 ...

machine-learning data-mining supervised-learning unsupervised-learning

asked Nov 25 '15 at 15:14

Tnatsissa H Craeser

551 silver badge5 bronze badges

Stack Exchange Network

current community

your communities

more stack exchange communities

Questions tagged [unsupervised-learning]

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

Can a novelty detection model overfit?

Combine two sets of clusters

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

Word2Vec vs. Sentence2Vec vs. Doc2Vec

Anomaly detection on time series

Better approach to assign values to determine potential fake sentences

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

Is Overfitting a problem in Unsupervised learning?

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

Estimating Predictive Uncertainty for unlabeled data

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

Best approach for this unsupervised clustering problem with categorical data?

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

Ideas for prospect scoring model

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

How do I interpret my result of clustering?

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

Document similarity

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

Unsupervised Clustering for n-length word arrays

How to JUST represent words as embeddings by pretrained BERT?

Theoretical treatment of unlabeled samples

Hot Network Questions

Questions tagged [unsupervised-learning]

Related Tags