Questions tagged [unsupervised-learning]

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

182 questions with no upvoted or accepted answers
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595 views

Recommended method for finding archetypes or clusters

I wish to cluster users together in a database, with each user represented by a number of features that are both discrete and continuous in nature. The aim is to define a small number of archetypal "...
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632 views

Computation of log-likelihood in semi-supervised naive bayes

I have the following 2 questions about log-likelihood computation in semi-supervised Naive Bayes. I have read on several documents online that, in every EM iteration of the semi-supervised Naive ...
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357 views

What enforces features diversity in RBM?

I'm working on an implementation of a Restricted Boltzman Machine (RBM). I made some tests on the MNIST dataset trying to learn a representation of the digit 2. My inputs are binary images. My aim is ...
4
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1answer
59 views

In variational inference on von Mises clusters, how to find a bound for the log-Bessel function?

This paper on von Mises clustering uses an upper bound on the modified log-Bessel function that I struggle to replicate. Taking results from this paper, the authors state: $$u\frac{I'_\nu(u)}{I_\nu(u)...
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891 views

Boosting in unsupervised learning - methods and use cases

I'm looking for methods and uses cases for applying boosting or other ensemble methods for unsupervised learning Examples of such methods and use cases are: Boosting density estimation Saharon ...
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856 views

Pre-training deep neural networks by supervised learning

When pre-training deep neural networks layer by layer, is it normal to pre-train the layers -which haven't been pre-trained by unsupervised training- by using supervised training before we train the ...
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207 views

Topological data analysis and evaluating dimensionality reduction

I did an exploration some time ago on using TDA tools to see how topological features change after application of some nonlinear dimensionality reduction methods. For example I found out that, for ...
3
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205 views

Neural Networks: utilizing weakly-labeled data to improve fully-supervised network?

The problem: I have built a fully-supervised CNN that localizes an object in different scenes. As you can imagine, it quite time-consuming to label data: I have to manually localize the object in an ...
3
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51 views

How to train intermediate filters/kernels in a stocastic spiking convolutional network using stdp?

So I am working on building a spiking convolution network for image classification (currently MNIST). Up till now I have trained the 1st layer filters by providing patches (same size as the filter) of ...
3
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380 views

How to build “supervised clustering” for neural networks?

I'm confused as to what the output would be. Consider the "blind source separation" problem. Let's say I have a ton of training examples where the input is the final cacophony of sounds as a sound ...
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82 views

Can collaborative filtering be cast as a classical regression problem?

Having the Netflix challenge in mind: collaborative filtering is typically presented as a matrix dimension reduction. My question is how does the problem relate to classical regression (supervised ...
3
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66 views

Integrating Prior estimates in Simrank Model

I am reading SimRank paper by Jeh and Widom which tries to find the similarity between objects based on the relationships between them. Effectively, SimRank is a measure that says "two objects are ...
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310 views

References on semi-supervised LDA

I'd like to perform semi-supervised LDA (Latent Dirichlet Allocation) in the following sense: I have several topics that I'd like to use, and have seed documents that relate to these topics. I'd like ...
2
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1answer
97 views

LDA/NMF Topic Modeling vs Topic Modeling using “skip gram” approach

I am having a little friendly debate with my coworker on how to properly/optimally do topic modeling. I am just using the regular traditional nmf/lda approach and he decided to do it using "skip grams"...
2
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1answer
63 views

Scaling data with different importance

I have 9 attributes: x1,x2,x3,x4,...,x9 and I know that the attributes x9 must have the same value in a cluster and the attribute X1 have more importance than others (x2,...,x8) I'm using Euclidean ...
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38 views

Differentiate Semi-supervised vs Transductive Learning?

Can someone explain the difference between transductive learning and semi-supervised learning? Or is semi-supervised learning a type of transductive learning? Transductive learning is when we do not ...
2
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1answer
75 views

Unsupervised clustering of sequence of events to subsequences

I have a big dataset of M sequences of [1 - N] events, where each event has multiple properties (start date, end date, location, ...
2
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0answers
74 views

Is there a well established algorithm to match two documents on a semantic level?

I have a set of documents from a wide variety of topics and I would like to retrieve the ones that are more similar to a new document provided. A search based on common words is not good enough, so ...
2
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1answer
399 views

Image feature extraction using an Autoencoder combined with PCA

Background: I have fairly large dataset of biomedical images (around 10,000 images) of 1920x1920 pixels (after cropping parts of black borders out). My task is to extract the 200 most important ...
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36 views

Understanding short animation about Dirichlet Process Mixture Model

On the wikipedia page of Dirichlet Process, there is the following video. I don't get the point of the video. My first impression was that the video was showing the fitting of one-dimensional data ...
2
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1answer
127 views

Custom Loss Function - Inducing sparsity

From the comments, I realized that my question wasn't clear enough, so I'll start with a short background. I am trying to construct an attention model that performs classification based on just a ...
2
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45 views

Performing clustering without a distance matrix

I have n vectors and a matrix of similarity scores between them (e.g. vector 1 score of similarity with vector 4 is 1.3, and with vector 7 is 2.3). This matrix is ...
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322 views

Clustering as a method to find and label classes for supervised learning

I'm working on a text classification project. We have around 300k documents (small, 1~2 phrases) and we don't know the set of labels or how many labels there are. The recommended approach to me was ...
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87 views

clustering objects in point cloud

I am currently working on point cloud data analysis, trying to label objects which are not ground, vegetation, etc. So far I tried many clustering algorithms, with moderate success. In my best model ...
2
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19 views

What are the simple methods to do an unsupervised cluster to stock return time series?

I am a student in finance and I am working on my thesis project. I am interested in doing a clustering to stock time series. I first read the paper 'Time-series clustering – A decade review' from ...
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870 views

Unsupervised anomaly detection - metric for tuning Isolation Forest parameters

I have a project, in which, one of the stages is to find and label anomalous data points, that are likely to be outliers. As a first step, I am using Isolation Forest algorithm, which, after plotting ...
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295 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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142 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 ...
2
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86 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 ...
2
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38 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 ...
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74 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 ...
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0answers
136 views

Random forest - extracting profiles

I'm using the good old decision tree (CRT or CHAID algorithm, depending on the situation) in order to predict voting behavior and extract some profile (e.g: Women who live in the suburbs, who are not ...
2
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0answers
270 views

State-of-the-art: unsupervised learning for patterns in text

My gut tells me tried-and-true approaches like k-means and Latent Dirichlet Allocation may no longer be state-of-the-art approaches for unsupervised learning with text data, what with models like ...
2
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0answers
320 views

Clustering of high dimensional data

I am having a data set with 54 independent variables .Most of them are having zeros it resembles like sparse matrix .How to cluster this kind of data and is there any data pre processing like Box cox ...
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0answers
315 views

How to do model selection in the unsupervised learning setting?

For supervised learning, we know the correct answers for samples, model selection is more easier, we can use k-ford cross validation (this site!) and etc. But for unsupervised learning, e.g. ...
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0answers
54 views

How can knowing test data improve the performance of face recognition?

I don't think knowing face recognition is necessary to answer this question, any suggestions are welcome. For face verification problems, usually images are first converted into fixed length vectors ...
2
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1answer
101 views

Any way to recognize pattern(such as Char and Number) from image without labeled data?

I am trying to build a captcha recognizer. I found CNN play very well if there are enough labeled data. For example, I use this https://github.com/lepture/captcha to generate 4 size char+number ...
2
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0answers
284 views

Metric for unsupervised recommender-system competition?

I have a data source containing millions of documents from a wide variety of business domains. We've aggregated the data such that we can easily find information using natural-language search queries. ...
2
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0answers
471 views

unsupervised methods for conditional random fields (CRF)

can any one explain that why CRFs are not applicable for unsupervised learning? thanks in advance
2
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0answers
90 views

EM for GMM similar to KMeans

Can we get the value of the latent variable for each training example while fitting a Gaussian mixture model by performing kmeans on the data set ? Further can we then estimate the other parameters of ...
2
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1answer
297 views

Unsupervised Learning on Multilevel/Multidimensional Data

I am working on a case-control study, where I for each individual have high dimensional data (like illustrated in the image). I would like to do both PCA analysis and Clustering on this data, but it ...
2
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0answers
640 views

kMeans unsupervised feature learning on multiple layers

I'm trying to develop an unsupervised feature learning pipeline. I have a train set with 512x512 images. I've extracted 16x16 patches, performed preprocessing steps (normalization and whitening). ...
2
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0answers
346 views

Automatic classification of outliers

I have the following plot of data: and I am trying to separate the main part of the data with the outliers that are far away from the main data (for example the data found at around x=250, around x=...
2
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0answers
616 views

Stopping Criteria for Pre-Training using Stacked Autoencoders

In stacked autoencoders during greed layer-wise training of individual autoencoders using gradient descent and backpropagation to minimize the reconstruction error(squared error or cross entropy) what ...
2
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1answer
141 views

Clustering Consumer data with over 100 variables and 50000 rows each

I am tasked with performing a clustering exercise for a consumer survey dataset with the hopes of finding distinct consumer segments. In the past, I've done it using a variety of techniques- ...
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0answers
1k views

Unsupervised Random Forest for Visual Codebook generation

I'm trying to apply the bag of visual words approach to make scene classification. I started to use k-means to generate my codebook, but rapidly discovered its limitations. From one codebook ...
2
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1answer
224 views

Heuristics for unsupervised or semi-supervised approaches to GIS coordinate data

I have a more conceptual/heuristic question about how to go about formulating a problem in order to take a semi- or unsupervised method of solving it. I'm working on a project with data collected ...
2
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0answers
132 views

Non-negative Matrix Factorization - Basic Question: Cluster Assignment

Given the result $V_{m\times n} \approx W_{m\times k} \cdot H_{k\times n}$, where columns of $V$ are data points and $m$ is data dimension, what is the function by which you assign the data points to ...
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98 views

Anomaly/outlier detection using fuzzy clustering

I understand that fuzzy clustering using FCM produces a membership matrix for the set of data points we feed to it. What characteristics will an anomalous cluster produced during this method have? (...
2
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0answers
79 views

Univariate clustering for longitudinal cohort

We have screening information on thousands of patients followed for several years. We also have their cancer outcomes, whether or not such cancers were identified by screening or were otherwise ...