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28 votes
Accepted

Word2Vec vs. Sentence2Vec vs. Doc2Vec

Well the names are pretty straight-forward and should give you a clear idea of vector representations. The Word2Vec Algorithm builds distributed semantic representation of words. There are two main ...
Himanshu Rai's user avatar
  • 1,848
26 votes
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Confused about how to apply KMeans on my a dataset with features extracted

For clustering, your data must be indeed integers. Moreover, since k-means is using euclidean distance, having categorical column is not a good idea. Therefore you should also encode the column ...
HonzaB's user avatar
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14 votes
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How can autoencoders be used for clustering?

Clustering is difficult to do in high dimensions because the distance between most pairs of points is similar. Using an autoencoder lets you re-represent high dimensional points in a lower-...
tom's user avatar
  • 2,248
13 votes
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Clustering high dimensional data

I don't think any of the clustering techniques "just" work at such scale. The most scalable supposedly is k-means (just do not use Spark/Mahout, they are really bad) and DBSCAN (there are some good ...
Has QUIT--Anony-Mousse's user avatar
11 votes

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

Take a look at these links- https://stats.stackexchange.com/questions/108743/methods-in-r-or-python-to-perform-feature-selection-in-unsupervised-learning http://www.jmlr.org/papers/volume5/dy04a/...
Ankit Seth's user avatar
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10 votes
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Does it make sense to train a CNN as an autoencoder?

Yes, it makes sense to use CNNs with autoencoders or other unsupervised methods. Indeed, different ways of combining CNNs with unsupervised training have been tried for EEG data, including using (...
robintibor's user avatar
10 votes

What is the difference between topic modeling and clustering?

The purpose of topic modeling methods is to discover the latent themes (topics) assumed to have generated the documents of a corpus. Topic modeling methods are built on the distributional hypothesis, ...
geompalik's user avatar
  • 411
10 votes
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K-Means vs hierarchical clustering

I would say hierarchical clustering is usually preferable, as it is both more flexible and has fewer hidden assumptions about the distribution of the underlying data. With k-Means clustering, you ...
tom's user avatar
  • 2,248
8 votes
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Ideas for prospect scoring model

I faced almost exactly the same scenario a year and a half ago -- basically what you have is a variation of the one-class classification (OCC) problem, specifically PU-learning (learning from Positive ...
Brandon Loudermilk's user avatar
8 votes
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Is Overfitting a problem in Unsupervised learning?

Overfitting happens when the model fits the training dataset more than it fits the underlying distribution. In a way, it models the specific sample rather than producing a more general model of the ...
DaL's user avatar
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8 votes
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Gaussian Mixture Models as a classifier?

Some unsupervised models can make predictions, but not ones that necessarily match the original class labels. Once a GaussianMixture model has been fitted, it can ...
timleathart's user avatar
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7 votes
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Is SVD non-linear while PCA (by eigendecompostion) is linear?

To the best of my knowledge no. SVD and PCA are both linear dimensionality reduction algorithms. Some nonlinear dimensionality reduction algorithms are e.g. LLE, Kernel-PCA, Isomap, etc. About t-SNE ...
Kasra Manshaei's user avatar
6 votes
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Combine two sets of clusters

To compare two LDA topics, you're really trying to compute the distance between two probability distributions. One such measure that's commonly used in these circumstances is the Hellinger Distance. ...
Thomas Cleberg's user avatar
6 votes
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Why will the accuracy of a highly unbalanced dataset reduce after oversampling?

Imagine that your data is not easily separable. Your classifier isn't able to do a very good job at distinguishing between positive and negative examples, so it usually predicts the majority class for ...
timleathart's user avatar
  • 3,940
6 votes

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

Accuracy is probably not a good metric for your problem. For the original dataset, if the model just makes a dummy prediction that all samples belong to the bigger class, the accuracy will be 83% (...
TQA's user avatar
  • 536
6 votes

Confused about the different aspects in Machine Learning

Good question and welcome to Datascience Imagine you have the tree as follows. ...
alpha_989's user avatar
  • 161
6 votes
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What does it mean by “t-SNE retains the structure of the data”?

You should break this down one step further: retaining local structure and retaining global structure. Other well-understood methods, such as Principal Component Analysis are great at retaining ...
n1k31t4's user avatar
  • 14.9k
6 votes

How to do feature selection for clustering and implement it in python?

Often people confuse unsupervised feature selection (UFS) and dimensionality reduction (DR) algorithms as the same. For instance, a famous DR algorithm is Principal Component Analysis (PCA) which is ...
mnm's user avatar
  • 411
6 votes
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Can a novelty detection model overfit?

Answering your question: yes, depending on the hyperparameters you choose, you could overfit the considered normal data, if you fit your separating hyperplane between normal and novel points being too ...
German C M's user avatar
  • 2,724
6 votes

How to do clustering assuring more than one class per cluster?

This is a very strange design: The goal is to train an ensemble classification model. In general there is no strong reason to use only subsets of the data to train the individual learners, let alone ...
Erwan's user avatar
  • 25.5k
5 votes

Intuition Behind Restricted Boltzmann Machine (RBM)

In addition to the existing answers, I would like to talk about this energy function, and the intuition behind that a bit. Sorry if this is a bit long and physical. The energy function describes a so-...
hbaderts's user avatar
  • 1,114
5 votes
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Intuition Behind Restricted Boltzmann Machine (RBM)

RBM's are an interesting beast. To answer your question, and to jog my memory on them, I'll derive RBMs and talk through the derivation. You mentioned that you're confused on the likelihood, so my ...
Armen Aghajanyan's user avatar
5 votes

How to use GAN for unsupervised feature extraction from images?

Typically to extract features, you can use the top layer of the network before the output. The intuition is that these features are linearly separable because the top layer is just a logistic ...
kenny's user avatar
  • 435
5 votes
Accepted

What is the meaning of spherical dataset?

In this case, a picture is a worth a thousand words. They literally mean data whose distribution on X,Y is roughly a sphere. Different clustering algorithms work better on different distributions. For ...
CalZ's user avatar
  • 1,663
5 votes
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Unsupervised feature reduction for anomaly detection with autoencoders

I have used stacked auto-encoders to reduce our 40 features step by step to 5 features and then output back to 40 features (some of my features were all zeros/ non deviating features). Training this ...
rishiehari's user avatar
5 votes
Accepted

What approach other than Tf-Idf could I use for text-clustering using K-Means?

You will likely see an improvement by using an algorithm like GloVe in place of Tf-Idf. Like Tf-Idf, GloVe represents a group of words as a vector. Unlike Tf-Idf, which is a Bag-of-Words approach, ...
JohnM's user avatar
  • 103
5 votes

Anomaly detection on time series

You can have a look here, where many open-source algorithms specifically for anomaly detection on time-series data (e.g. metrics) are collected, both for online of offline settings. Almost all of ...
rob_med's user avatar
  • 480
5 votes
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Anomaly detection thresholds issue

Instead of mean and standard deviation, you could estimate the median and mean absolute deviation. The median is immune to outliers, and the MAD should be at least more robust than the standard ...
goopy's user avatar
  • 76
5 votes
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What is major difference between different dimensionality reduction algorithms?

A detailed answer would require many pages of explanation, but I think a brief answer may point to the right direction for further research. First of all the choice of dimensionality reduction ...
Nikos M.'s user avatar
  • 2,373
4 votes

generalized likelihood ratio test (GLRT)

Likelihood-ratio tests are a mainstay of classical hypothesis testing. The idea is to form the likelihoods of the two hypotheses under consideration, and choose the one with the highest likelihood if ...
Emre's user avatar
  • 10.5k

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