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
0
votes
1answer
625 views

How to recreate T-SNE dimensions deterministically?

So I have a set of 3000 features from which I would like to generate clusters. I passed my features through the T-SNE algorithm to reduce dimensionality to 2 features, and clusters are really visible ...
3
votes
1answer
2k views

Good books on unsupervised learning [closed]

I am looking for a good book about unsupervised learning that goes beyond the typical k-means and hierarchical clustering algorithms. Practical implementations in R or Python will be a plus. ...
3
votes
2answers
203 views

which NN should I use for Time-series dataset, whose pattern change as time goes

I am analyzing a time-series dataset using (supervised) tensorflow deep learning. The tensorflow code is given a series of inputs, and based on each input, the NN has to predict output value in near ...
0
votes
1answer
48 views

Bandwidth selection Kernel Density Estimation

I want to do KDE on data that are not necessarily normal using Gaussian kernels. In KDE in wikipedia an expression for the bandwidth is given when the underlying distribution of the data is gaussian. ...
1
vote
2answers
386 views

Confused about the different aspects in Machine Learning [closed]

After reading different articles about ML and algorithms, scientist tends to use different words when describing the different aspects in ML. So now I'm a bit confused myself and I hope you can ...
0
votes
1answer
3k views

Understanding Contrastive Divergence

I’m trying to understand, and eventually build a Restricted Boltzmann Machine. I understand that the update rule - that is the algorithm used to change the weights - is something called “contrastive ...
2
votes
2answers
101 views

Which is the best Machine learning technique for this Load forecasting problem?

I am trying to use Machine Learning to predict the load of a residence at any point in time for a whole year. I have past data pertaining to that house. So I have the training data and I need the ...
1
vote
0answers
71 views

Categorical data with order and blanks, is frequent dataset or k-modes a better option?

I have a dataset that's purely categorical: for each item it's ranked across a set of attributes, whether it's easy, moderate or difficult. But there are blanks if the item doesn't have the ...
0
votes
1answer
65 views

Can You Purposely Bias A Clustering Model?

We have a large amount (Billions) of high cardinality, mixed nominal & numerical data, and are performing some clustering on it as an experiment. There is a small subset of these data, however, ...
1
vote
1answer
649 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 ...
2
votes
3answers
6k 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 ...
2
votes
4answers
71 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
0answers
45 views

Conceptual clustering with sklearn?

How can I perform conceptual clustering in sklearn? My use case is that I have English Wikipedia articles that I'm doing unsupervised learning on (tfidf -> truncated svd -> l2 normalize), and I'd like ...
2
votes
1answer
628 views

Is it possible to make a label automatically in supervised learning(Machine Learning)?

My background knowledge: Basically, supervised learning is based on labeled data. Using the labeled data, the machine can study and determine results for unlabeled data. To do that, for example, if we ...
4
votes
2answers
5k 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 ...
1
vote
1answer
47 views

Help me understand how word-as-vector representations are constructed

Let's suppose I have a big list of words. I want to turn this list into a vector space of dimension $N$ such that each word is a vector in this vector space. But I have no idea how to go about with ...
4
votes
2answers
77 views

When using an unsupervised alogirthm, what is the “learning” part since it belong to machine learning field?

I had a brief experience with machine learning by using a clustering algorithm, i also read the basic ideas and calculations of a simple classification algorithm. Now, i would read more about "machine ...
1
vote
1answer
269 views

What is the advantage of using Dunn index over other metrics for evaluating clustering algorithm? [closed]

There are many metrics to evaluate clustering algorithm like Calinski-Harabaz Index, Dunn index, Rand index, etc. Are there any advantage of using Dunn index over other metrics for evaluating ...
1
vote
1answer
52 views

Machine Learning to predict risk of items [closed]

I'm trying to find out what I need to research and start learning to try and apply machine learning to this problem: In multiple offices I have 20 chairs, all of these chairs will need to have a ...
5
votes
1answer
14k views

K-Means vs hierarchical clustering [closed]

What use cases does it make more sense to use hierarchical clustering as opposed to K-Means and vice versa?
5
votes
3answers
1k views

To detect unauthorized access using outlier detection

I am working on project where my task is to find unauthorized access using any machine learning technique. Let me clear my problem definition. UserA access website using chrome browser from windows ...
1
vote
0answers
2k views

General unsupervised learning strategy when using convolutional autoencoder (CAE)

I am working on implementing an autoencoder for unsupervised learning, and I have some questions about the overall process. From what I was reading here, @rjpg suggests the following general approach: ...
7
votes
1answer
3k views

What is the difference between topic modeling and clustering?

I know that topic modeling and clustering are related, but not similar techniques. Can anyone suggest what are the main differences?
1
vote
1answer
528 views

Unsupervised clustering of unstructured text by document type

I have 100,000+ PDF healthcare documents from which I have extracted text. I would like to cluster these documents by type (e.g. pathology report, doctor visit notes, prescription orders, etc.) The ...
0
votes
2answers
243 views

Unsupervised text clustering using a driving list

I want to apply unsupervised clustering on a set of short texts, which I need to divide into 2 clusters. Also I know that one of my clusters is likely to contain some words (non-exhaustive list) and ...
1
vote
1answer
23 views

Use 1 or 2 norm for Voronoi vector quantization?

I have a script from a lecture. Basically it says that based on the Voronoi partitioning we identify the corresponding (nearest) class $w_k$ to a vector $x$ where $\left| {{w_k} - x} \right| = \mathop ...
9
votes
2answers
6k views

How can autoencoders be used for clustering?

Suppose I have a set of time-domain signals with absolutely no labels. I want to cluster them in 2 or 3 classes. Autoencoders are unsupervised networks that learn to compress the inputs. So given an ...
0
votes
1answer
203 views

Clustering documents - how to evaluate results?

I'm using DBSCAN clustering on a set of documents. The documents' content was converted to TF-IDF matrix, and I'd like to find consistent ways to evaluate the clusters when no added information is ...
1
vote
1answer
631 views

Deriving the update rule for mean in k-means clustering

I have a homework question that ask me to derive the update rule from the distortion metric: It then says: "The update rule is derived by computing the gradient for each element of the k-th mean and ...
5
votes
1answer
534 views

Unsupervised feature reduction for anomaly detection with autoencoders

I am collecting a big number of generated numeric features for the task of unsupervised anomaly detection. I can assume that all training data is considered normal. I expect some of the generated ...
0
votes
1answer
415 views

Automatic question categorization when we know important words in each category

I am currently working on a question categorization problem where I automatically want to assign a category to the question. The question set I have is unlabelled. The categories for the problem are ...
1
vote
1answer
439 views

Unsupervised learning if existing image captions match the images

I need to train a system on a large set of images and associated captions to determine which (image, caption) pairs are correct and which are not. I don't have any labeled pairs, but I can assume that ...
7
votes
6answers
651 views

Clustering algorithms for high dimensional binary sparse data

I have a dataset with 10,000 genes like below ...
2
votes
1answer
37 views

Does Non Negativity Constrains increases the estimation error

I have been working with Tensor and matrix Non negative constrained algorithms. I have never seen a non negative constrained algorithm (ex. Non Negative Tucker Decomposition NTD) with error that is ...
2
votes
1answer
79 views

Can clustering my data first help me learn better classifiers?

I was thinking about this lately. Let's say that we have a very complex space, which makes it hard to learn a classifier that can efficiently split it. But what if this very complex space is actually ...
4
votes
1answer
344 views

Clustering for high dimensional data

I am have a data set with 52 variables. Most of them have zeros, it resembles a sparse matrix. How can I cluster this kind of data and are there any special types of clustering? I am attaching pca ...
1
vote
1answer
64 views

Stuck implementing k means for big and small dogs - dodgy results

my algortithm isnt working. The code seems to make sense and everything but im just not convinced with the results. I fell like the centroids should be amongst the data more, sort of central, but they ...
-1
votes
1answer
74 views

Text standardisation for manually entered data

I am working on a project that involves dealing with manually entered text data. I have a dataset of customs records where the customs officers manually enter the name and address of companies ...
3
votes
3answers
681 views

ML algorithms for defining NORMAL user behavior [closed]

I am trying to create a model that would capture the usual behavior of a user: i.e., create a model for user profiling. I do have 2 million rows of data indicating normal behavior of a user and want ...
4
votes
1answer
3k views

How to find patterns in a series of timestamps

I have a series of timestamps that represent the time a user clicked a certain button. My goal is to detect the automated clicks, so I need to find recurring patterns in the data that may point to an ...
4
votes
1answer
608 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 = ${...
2
votes
1answer
4k views

Loss function in GAN

Since the aim of a Discriminator is to output 1 for real data and 0 for fake data, hence, the aim is to increase the likelihood of true data vs. fake one. In addition, since maximizing the likelihood ...
1
vote
1answer
193 views

Training the Discriminative Model in Generative Adversarial Neural Network

What I know so far in DCGAN is that a discriminator is trained using the labeled data (so maybe that occurs before training the generative model). Also, I know that there is race between the generator ...
4
votes
2answers
1k views

What is the meaning of spherical dataset?

In the following article, one of the statement is as follows: The K-means algorithm is effective only for spherical datasets What does spherical dataset mean?
0
votes
1answer
92 views

About A technique mentioned on ESL to transform an unsupervised into supervised

I'm reading page 495 of Elements of Statistical Learning. Here we discuss a technique for transforming the density estimation problem into one of supervised function approximation. This forms the ...
2
votes
1answer
556 views

What is the purpose of the discriminator in an adversarial autoencoder?

This is specific to the generative adversarial network (GAN) proposed in A. Makhzani et al. "Adversarial Autoencoders". In a traditional GAN, the discriminator is trained to distinguish real samples ...
2
votes
1answer
1k views

Outlier detection on categorical network log data

I am working with a completely categorical network log data that consists of source ip address, destination ip address, source port, destination port, protocol. Data Preprocessing performed : ...
20
votes
1answer
20k 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. ...
1
vote
1answer
594 views

News topic detection and categorization

If I want to get how many and what kind of topics are covered by New York Times each week from a bag of words model(All the news covered by NYT in a week) how should I approach? Using traditional ...
3
votes
1answer
148 views

Probability for label correctness in semi-supervised learning

I am aware of the existence of semi-supervised learning approaches, such as the Ladder Network, where only a subset of the data is labeled. Are there any methods or papers which consider correctness ...

1
3 4
5
6 7