Questions tagged [unsupervised-learning]

Finding hidden (statistical) structure in unlabelled data, including clustering and feature extraction for dimensionality reduction.

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1answer
232 views

Tips on preparing data for training on neural networks? [closed]

Still feeling a bit new to the world of neural networks. I am working with a CNN model right now (working with Keras), and would like to train it to identify certain types of objects from a dataset. I ...
1
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1answer
42 views

k nearest neighbor classification algorithm

I'm currently studying about K nearest neighbour algorithm. I understand the basics of it. The problem I have is I have the below equation given in a slide and do not understand the purpose of it. ...
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1answer
88 views

Violating “compactness” in single linkage hierarchical agglomerative clustering

While I was studying Hierarchical Agglomerative Clustering in the book Elements of Statistical Learning in the chapter Unsupervised Learning, I came through the following : The statement The ...
6
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2answers
1k views

Deriving Multiplicative Update Rules for NMF

How to derive the multiplicative update rules for the non-negative matrix factorization problem given by Lee and Seung. Minimize $\left \| V - WH \right \|^2$ with respect to $W$ and $H$, subject ...
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0answers
185 views

Why does the Independent Component Analysis require non-gaussian?

This I found on google while I was going through the Independent Component Analysis in unsupervised learning. Let x = As where A is the Mixing Matrix. So, Lets assume that s here is Gaussian ...
5
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1answer
261 views

What is mean by the non-gaussianity in the independent component analysis(ICA)?

What is mean by non-gaussianity in ICA? Why do we use in ICA? How is Non-Gaussianity is an important and essential principle in ICA estimation? Following is the statement I found in a research paper....
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1answer
1k views

Unsupervised Outliers detection on time series

So I am looking ways to improve my current implementation of detecting outliers in work schedule. My data set is badge swipes for people. The current implementation finds outliers on in-times and out-...
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3answers
543 views

What is the precise definition of unsupervised learning?

Let's look at a special case: Generative Adversarial Networks (GANs). (For those who don't know what a GAN is: for this purpose they are two neural networks that are trained using user generated ...
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1answer
33 views

Determine the most important documents for supervised learning

I have somewhat of a general/high level question. Assume I'm doing supervised machine learning on some text data (tweets for example) and categorizing the documents to a certain taxonomy (multi-class ...
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0answers
21 views

Unsupervised Clustering of words from documents in 2 clusters

I'm new to these fields of Machine Learning, and I'll have to use unsupervised clustering of texts, that is make two clusters of words from a document, but without using the widely used K-Means ...
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0answers
23 views

Choosing the right model to learn [closed]

I'm new to the data science world, and I hope to solve a problem using deep learning methods, I started learning how FNN and CNN work and when I saw how many models and methods are the I got a bit ...
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1answer
29 views

Self-Organizing maps : is a N*M grid the same as a M*N grid? (with M different from N)

Self-Organizing maps : will a N*M grid give the same results as a M*N grid? (with M different from N)?
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1answer
337 views

Assigning weights to variables to calculate rank/score of an Agent

I have data on Agents behavior history. I want to score each of these Agents based on the attributes. Attributes are both Categorical and Continuous.For this, I want to calculate the score by ...
2
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0answers
244 views

Using Naive Bayes classifier for unsupervised learning

I was going through this article to learn about how the EM algorithm can be used to use the Naive Bayes algorithm for unsupervised learning. Suppose we have the following data without labels: 1 0 1 1 ...
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1answer
111 views

Optimal hidden units size

Suppose we have a standard autoencoder with three layers (i.e. L1 is the input layer, L3 the output layer with #input = #output = 100 and L2 is the hidden layer (50 units)). I know the interesting ...
2
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1answer
215 views

What are some applications of unsupervised HMMs?

Supervised HMMs can be applied to many problems like POS tagging and OCR (optical character recognition). I've learned that HMMs can be trained unsupervisedly using EM (Baum-Welch algorithm), what ...
2
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1answer
535 views

What are supervised learning and unsupervised learning from a connectionist point of view

The general concept of supervised learning and unsupervised learning is very clear. In supervised learning, the decision on the unlabeled data is done after learning a classifier using available ...
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1answer
502 views

Comparison between hierarchical clustering and principal component analysis (PCA)

I just read a article about the comparison between PCA and hierarchical clustering, but I cannot find the strengths and weakness of clustering compared Principal Component Analysis, what about other ...
2
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1answer
387 views

How can unsupervised learning be performed by connectionist (neural network) algorithms?

Connectionist models of the mind (a subclass of which are neural networks) can be used to model a number of different behaviors, including language acquisition. They consist of a number of different ...
2
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1answer
328 views

If regression is supervised learning is correlation unsupervised learning?

Regression is often given as a simple example for supervised learning because you have a dependent variable and try to build a model with the independent variables. Could you say that correlation is ...
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1answer
27 views

NLP packages to check similarity between 2 sentences [closed]

Are there any NLP packages that can help to identify if two words or sentences are related to each other in some way? Like helmet is related to ...
1
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1answer
25 views

Are hot machine learning solutions -like the Show, Attend and Tell paper- instances of unsupervised learning?

The Show, Attend and Tell paper describes a solution to image captioning. I am wondering: Is this novel machine learning application and instances of (semi-)supervised or unsupervised learning (in ...
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0answers
111 views

Supervised vs unsupervised for anomaly detection

My problem is detecting the good/bad car reparations given the repair cost. And then I can rank the car garages based on the percentage of 'good' repair he got. A first simple model is using ...
1
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1answer
293 views

Validating Clustering by Considering the Ratio of Intra-cluster to Inter-cluster Distance

I'm trying to evaluate a clustering method by looking at the ratio of the mean intra-clustering distance (the average distance between points in the same cluster) to the mean inter-cluster distance (...
5
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1answer
672 views

GANS: Using Discriminator for prediction

In the past few years, GANs have been a hot topic and a lot of papers are being published every year regarding GANs. But I always see that either the results of the generator are being shown (sample ...
0
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1answer
55 views

Isn't computing the “tractable error” in Restricted Boltzmann Machines (RBM) intractable?

Let $v \in \{0,1\}^M$ be the visible layer, $h \in \{0,1\}^N$ be the hidden layer, where $M$ and $N$ are natural numbers. Given the biases $b \in \Re^M$, $c \in \Re^N$ and weights $W \in \Re^{M \times ...
4
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2answers
204 views

Which criteria to use to determine cluster centroids?

I have 3-dimensional datapoints I want to cluster. The idea is to do this in an unsupervised fashion with neural networks for a research project. To see how the system performs I'm trying with ...
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0answers
109 views

Unsupervised 1D clustering with sparse data

I am developing an unsupervised 1-dimensional clustering algorithm to detect regions of a protein in which genetic variants found in a population tend to concentrate. The analyzed data structure is a ...
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1answer
843 views

Passing Time Series Data to an Unsupervised model

I have a particular segment of temporal data for 3 days. It looks like this: ...
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0answers
35 views

What does it mean to do data exploration?

I have been trying to learn machine learning via a data set consisting of animal positions taken from a tracker (as a GPS is out of financial reach for me). The ultimate aim is to be able to describe ...
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2answers
560 views

Linkage method for hierarchical clustering of binary data

I need to cluster datapoints that are represented as a binary vector, using hierarchical cluster. I chose the manhattan distance and am trying to figure out how to choose the "best" linkage method. I ...
3
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3answers
846 views

Clustering numeric, categorical, and multivalue categorical data

I have data that look like this: ...
2
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0answers
138 views

Can a neural net with unsupervised learning be used for detection of player formations in soccer?

I'm having a concrete problem I'm trying to solve but I'm not sure in which direction I should go. Goal: Identify formation of a soccer team based on a static positional data (x,y coordinates of ...
1
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2answers
46 views

is finding danger zones in a map considered as clustering problem?

if I have a data-set of places where accidents happened in certain city , is identifing danger zones in that city considered as clustering problem ? if for example I use KMeans , I would have to pass ...
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1answer
492 views

In it necessary to split train, test, validation dataset for unsupervised machine learning algorithm (eg. autoencoder)?

Generally in supervised machine learning algorithms, the model performance is measured splitting train, test, validation set. But in case of unsupervised method , like autoencoder, is it necessary to ...
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0answers
139 views

Spectral Clustering using Negative Euclidean Distances

In most spectral clustering papers I've seen (von Luxburg's tutorial, Michael Jordan's NIPS paper, and some papers that predate those), they like using the affinity matrix generated by the Gaussian ...
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1answer
627 views

Unsupervised learning examples in Matlab

I am trying to classify ECG data into abnormal and normal using unsupervised learning methods in Matlab. The problem is that whilst I am used to supervised learning algorithms, I have never seen how ...
2
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0answers
80 views

Clustering data sitting close to corners of an N-dimensional parallelepiped

I am looking for a method of clustering data that are close to the corners of an $N$-dimensional parallelepiped (but I don't know the vectors spanning it). Is there a good method for finding ...
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0answers
114 views

Classifying mouse gesture patterns based on time series of mouse coordinates

Hint: I am a programmer, but not a machine-learning guy, so be patient with me ;) I am currently working on a side project for finding patterns in mouse movement data. Example of patterns I am ...
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0answers
64 views

Reinforcement learning with 'actions' and unlabelled data

I am interested in researching a machine-learning algorithm for trading on Forex, after being inspired by several papers that I read. I want to do this more out of a love of computing and forex than ...
2
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2answers
157 views

Where do artificial neural networks belong in the 'taxonomy' of statistical learning methods?

I'm a non-stats person trying to learn more about statistical learning methods, and to organize my thinking I am trying to construct a mental taxonomy of the methods I'm learning about. For instance: ...
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1answer
129 views

Oja's rule gives unit eigenvector

Does Oja's rule for normalized Hebbian learning always result in a unit eigenvector which corresponds to the largest eigenvalue? Or are there any specific conditions or assumptions under which this is ...
3
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5answers
595 views

An algorithm similar to (or based on) K-means that do not require the 'k' number of clusters

These days I'm using a lot (and discovering) nice ways to use k-means' clustering. For example, clustering word embeddings (word2vec vectors) to find synonyms or clustering doc vectors (doc2vec) to ...
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1answer
46 views

Extracting common sequences from time sequence data

I have a large number of time ordered location traces that I'd like to extract common sequences from. These locations are mapped from latitude, longitude pairs to a 2D aggregation bucket to handle ...
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0answers
21 views

Feed guess features into unsupervised learnin classification?

I've got an completely unlabeled dataset and my task is to classify it into positive and negative, two categories. As the data is unlabeled, I have to choose unsupervised classification; however, we ...
1
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1answer
135 views

Binary Sparse Coding

In this binary sparse coding paper referenced in the Goodfellow/Bengio/Courville deep learning book (https://fias.uni-frankfurt.de/~bornschein/papers/HennigesEtAl_lva2010.pdf), the parameter $\pi=p(...
2
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0answers
33 views

Automatic fitting of normalization constant as a parameter in noise contrastive estimation

In the paper on Noise Contrastive Estimation, the authors define a parameterized density function $p_m^0\left(x;\alpha\right)$ to estimate the unnormalized PDF of the data, and then further define a ...
1
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1answer
573 views

Unsupervised learning methods on unlabeled data?

I'm facing with a challenge of unsupervised classification of unlabeled data. The case is, I have circa 1.2 million vehicle warranty claims, and must develop a classification model to tell whether ...
6
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4answers
669 views

t-SNE dimensions as additional predictor variables

This question could also (maybe) relate to PCA. I built a supervised RandomForest on a dataset that I'm currently working on - the actual V Prediction $R^2$ was holding around 80% across many CV ...
2
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0answers
72 views

Visualizing neural network inferences

I know this is an ongoing and hard question to answer, but if anyone has experience in this then please share your knowledge. Suppose I have made a neural network with the task of predicting an ...