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Questions tagged [unsupervised-learning]

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

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623 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 ...
6
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0answers
560 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 "...
5
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0answers
295 views

What are the votes in R's unsupervised random Forest?

I’m trying to better understand unsupervised random forests. An important part of understanding unsupervised random forests is being able to assess how good / appropriate a given forest is. For ...
5
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0answers
350 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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0answers
849 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 ...
3
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0answers
194 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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0answers
198 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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0answers
770 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 ...
3
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0answers
50 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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0answers
350 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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0answers
79 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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0answers
65 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 ...
3
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0answers
303 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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0answers
42 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 ...
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0answers
32 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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0answers
36 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 ...
2
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0answers
16 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 ...
2
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0answers
216 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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0answers
131 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 ...
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0answers
78 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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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 ...
2
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0answers
71 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 ...
2
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0answers
119 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
255 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
299 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 ...
2
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0answers
269 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
53 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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0answers
277 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
445 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
85 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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0answers
612 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
345 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
577 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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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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0answers
128 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 ...
2
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0answers
95 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
78 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 ...
2
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0answers
205 views

Computation of Maximization probabilities of the EM algorithm

I have implemented a semi-supervised Naive Bayes that makes use of the EM algorithm to iteratively learn from unlabeled data in a text classification problem, but I am not sure of the processing done ...
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0answers
25 views

How to measure correlation between two groups of variables?

I have a data set that contain 75 variables of football players . These 75 variables basically measures two different types of information. 30 of those variables related to bio metric information ...
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0answers
8 views

Is there a representer theorem for unsupervised learning (to justify kernel density estimation)?

In supervised learning, we get a representer theorem by considering regularized losses of the following form: In Kernel Density Estimation, we simply directly assume densities of the form Could this ...
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0answers
15 views

Predicting user behaviour based on transactional data - flagging “risky” behaviour

Firstly, I'm not sure if this is the right instance of StackOverflow to post on so feel free to ask me to put it elsewhere. I am exploring the concepts of clustering and "unsupervised" learning for ...
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0answers
12 views

How to interpret LDA (Latent Dirichlet Allocation)?

Say I want to run topic modeling with LDA on The 20 newsgroups text dataset. So basically a dataset with texts where every text belongs to one of 20 categories. I want the LDA to split the documents ...
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0answers
11 views

Find a set of k non-negative vectors that explain most of the variance of the dataset?

I have a set of securities and I am looking for long-only portfolios that explain most of the variance of the set of securities. If it weren't for the long-only requirement, I could have used ...
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0answers
36 views

How do unsupervised credit scoring models that don't consider historical financial data work?

There seems to be a number of startups (Zest Finance, Credolab etc.) that provide credit scoring schemes that rely exclusively on alternative data without considering users historical financial data ...
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0answers
11 views

How do I calculate ensemble-averaged PCA scores?

For context, please see https://arxiv.org/pdf/1808.00084.pdf (page 6) I am trying to replicate the results of the above link, which uses dimensionality reduction to observe a phase transition in an ...
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0answers
175 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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0answers
43 views

clustering objects in point cloud

I am currently working on point cloud data analysis, trying to label objects which are not ground or vegetation e.t.c. So far I tried many clustering algorithms, with moderate success. In my best ...
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0answers
19 views

Performance of Hierarchical Temporal Memory on unsupervised online anomaly detection problems

I'm looking for an algorithm to detect anomalies in streaming data (server metrics). The detection needs to be near-real time and unsupervised (labeled data will never be available, unfortunately, and ...
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0answers
22 views

When applying PCA to a dataset consisting of regression coefficients, should one use PCA on correlation or on covariance?

This is a follow-up question from the post: PCA on correlation or covariance? The accepted answer quotes: You tend to use the covariance matrix when the variable scales are similar and the ...
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0answers
53 views

What exactly is semisupervised learning?

I have come across two descriptions of what semisupervised learning is, where one would have a small set $\mathcal{L}$ of labeled data and a larger set $\mathcal{U}$ of unlabeled data. The first ...