Questions tagged [semi-supervised-learning]

Semi-supervised learning refers to machine learning tasks using a mix of labeled and unlabeled data. The goal is to learn a mapping from inputs to outputs, or to obtain outputs for particular unlabeled inputs. The unlabeled data is used to learn about underlying structure of the inputs, which can improve learning about the relationship between inputs and outputs. Semi-supervised learning involves elements of both supervised and unsupervised learning.

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Semi-supervised learning from longitudinal data

I wanted to get experts' opinion on the following scenario. I am analysing longitudinal biomarker data where serial measurements were collected over time and in the last time point we know exactly who ...
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15 views

Is There a General Confidence Measure for Single-View Semi-Supervised Regression?

I am experimenting with semi-supervised regression, but I cannot figure out how to compute confidence measures for the unlabeled dataset. I am reading a review paper, and it says that I am supposed to ...
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What are the SOTA Visual Representation Learning architectures for binary images?

I want to learn the visual representation of binary images such as: This may later be used for the shape classification problem. I have read 2 state-of-the-art visual representation learning ...
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Conditional log-likelihood in Semi-supervised learning by entropy minimization

learning about the basics of semi-supervised approaches I stopped on a the paper "Semi-supervised Learning by Entropy Minimization" [1]. The Equation (1) is not so clear to derive whereas ...
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14 views

Lots of unlabeled data and small set of labelled data of one class [closed]

Does anyone have suggestions for specific algorithm or implementation for labeled data of only one class and unlabeled data that can be from either classes? And I'm unsure what is the proportion of ...
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17 views

Poor accuarcy score for Semi-Supervised Support Vector machine

I am using a Semi-Supervised approach for Support Vector Machine in Python for the image classification from PASCAL VOC 2007 data. I have tried with the default parameters from the libraries and also ...
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1answer
10 views

Is it possible to alter binary classification models to do postive-unlabeled learning in Pyspark?

I'm learning how to use pyspark, and I'm wondering if it has any ways to implement positive-unlabeled learning? From searching this question I haven't been able to find any examples specific in spark ...
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20 views

Semi-supervised VS Self-taught learning

I want to build a Speaker Identification model and I am wondering what is the best for the feature extracting step: Using unlabeled examples from the same distribution as labeled ones (we can use the ...
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25 views

Semi-Supervised Hierarchical Topic Model

Problem statement: I'm looking to label some data with topics. These topics have a hierarchical structure (3 layers deep at maximum, but I have leaf nodes 2 layers down as well) that I have been given....
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16 views

Problem with a dataset not being properly labelled

I have a labelled dataset but these classes are not perfect. Some classes should be combined into one, whilst others have too few data-points for training. My main concern is the former not the latter....
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19 views

semi-supervised classification with a single label

I have a dataset of 1800 entries with about 40 features (some binary, some numerical). Of the 1800, 12 are known to be good for my goal; and the rest are unknown. Of the 1800 only 25-30 of the entries ...
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26 views

Is it OK to accept overfitting when encoding categorical variables with WOE to calculate similarity (Positive and Unlabeled model)

Currently, I am working on a Positive and Unlabeled "classification" (similarity) project. The model need to calculate similarity score between "Target population" (learning ...
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70 views

Q-function in Q-Learning

I ran into solved old-exam question as follows: My notes tell me that option b is correct but I think option d is correct. is there any idea why (b) is correct?
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148 views

Semi Supervised learning vs Supervised

I am trying to understand the mathematical properties of supervised learning and semi-supervised learning. Let us consider the case for the mean $\mu$. Then the supervised learning estimator can just ...
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198 views

How to determine equation of hyperplane for SVM?

Assume we have only two features in our training dataset that is already classified into class C1 and class C2. The transposes of the feature vectors are given below for each class: C1: [2 6], [1 1], [...
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76 views

What is noise-tolerant learning?

I was reading this Development and validation of phenotype classifiers across multiple sites in the observational health data sciences and informatics network and came across the below paragraph. Can ...
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1answer
60 views

Evaluating multiclass imbalanced problem per class

For a multiclass imbalanced problem, accuracy is not a good metric to evaluate model performance. Equally, accuracy is a global ...
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1answer
221 views

Training samples with no labels: To include or not to include?

I am working on a multi-label classification problem. Each sample is capable of taking more than a single label. Sometimes samples don't have any labels associated with them. My dataset has 50% ...
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18 views

Latest research and explanation on how semi-supervised learning is performing better than supervised?

So in AAAI 2020 also semi-supervised learning is given the push. There are some intuitive reasoning provided by people but since the research is so fast, I wanted to know actually what is the latest ...
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122 views

Evaluating Semi-supervised Learning

Is there a standard procedure to evaluate a semi-supervised learning algorithm? Say if I have a set of labelled data (500 spam & 500 non-spam), and a set of 50,000 unlabelled data. Theoretically, ...
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1answer
17 views

Active learning with a unlabelled pool - standard references & model-based labelling of the pool?

I'm looking into active learning for a multi-class classification problem, where there is a large pool of unlabelled data. I start out with a small set of labelled data and can labelled some more of ...
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What's the intuition behind contrastive learning or approach?

Maybe a noobs query, but recently I have seen a surge of papers w.r.t contrastive learning (a subset of semi-supervised learning). Some of the prominent and recent research papers which I read, which ...
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72 views

Semi supervised learning with partially unobservable labels

As I understood the concept of semi-supervised learning is to train a classifier on the minimal available subset of correctly labeled data in order to predict the labels of a greater previously ...
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1k views

Can we say that RNN for time series is an example of semi-supervised learning?

I am learning neural nets, esp. focusing on RNN for my research problem. This question has nothing exactly to do with my research. With my understanding of RNN, I can think of it as an example of ...
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1answer
117 views

Does it make sense to use feature selection methods to reduce dimensionality for unsupervised clustering?

If I have a dataset that is labeled with positive and negative examples, and I'd like to cluster (i.e. unsupervised) only the positive examples, does it make sense to reduce dimensionality using ...
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48 views

What is it called to cluster some inputs, then classify other inputs into those clusters?

I am learning about the problem of whole-book recognition, which is tangential to optical character recognition. Some of the strategies used to identify printed characters rely on first unsupervised ...
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44 views

How do you train a model on a dataset that's unlabeled but we know the percentage of every class?

Say we have a data set that's pictures of apples and oranges, but we don't know which is which. However the data is organized in such a way, that for some groups of images we know how many of them are ...
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Need some help understanding the factorised posterior in semi-supervised generative modelling

I am having a bit of trouble with the derivation in Kingma's semi-supervised generative modelling paper for the M2-model. The M2 model assumes a probabilistic model where the data $x$ is generated by ...
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36 views

Filling in Missing Data for Biological Experiment

I am trying to implement a semi-supervised learning model with biological data. In my case, I'm using features from DNA. I have a number of experiments each with many observations. Each observation ...
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102 views

In semi-supervies learning, is "low density separation" the same thing as "pseudo-labelling"?

I'm looking into the different methods of semi-supervised learning. In the wikipedia page, one of the methods described is called "low-density separation", where we attempt to minimize this loss ...
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2answers
204 views

Semi-supervised objective function VAE

In Kingma's paper on Semi-supervised learning https://arxiv.org/pdf/1406.5298.pdf, we are shown equations for the ELBO for the semisupervised case, however I am having a hard trying to derive the math ...
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1answer
112 views

Weak Supervision - training generative model without knowing the true label

Recently I've been reading about weak supervision. I understand most of the concept details, there's one thing that is not clear to me though. In the generative model part (creating generative model ...
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1answer
498 views

Is there something more effective than ladder networks for semi-supervised learning?

The paper Semi-Supervised Learning with Ladder Networks by Rasmus is famous and interesting but a bit old now. Did researchers find any better option for semi-supervised learning ? For example, what ...
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227 views

What derivative to use in Gradient boosting decision tree for a semi-supervised model

I am trying to build a semi-supervised prediction model with a Gradient Boosting decision trees. The learning phase is done using the following input: $X \in \mathbb{R}^{n\times p} $ $O(X) \in \...
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131 views

influence of oversampling on Semi-supervised multi-label learning

I have suggested a semi-supervised approach for the hierarchical multi-label classification task. I have included the MLSMOTE oversampling technique as a pre-processing step, and then evaluate the ...
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19 views

What is the convergence criteria of a semi-supervised learning algorithm

I would like to know about when to stop doing semi supervision? for example, I learn a classifier from a small dataset, and then I use it to label a pool of unlabelled dataset. How long should this ...
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1answer
112 views

Inference can be the goal of an unsupervised learning method or a semi-supervised learning method or even more of a reinforcement learning method?

I am new to machine learning, and I am reading a pair of machine learning books. These references talk about 2 different learning approaches: Prediction and inference, I understand the difference ...
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157 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 ...
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1answer
60 views

Can we use result of cluster analysis (e.g. K-means) as the input to train a classifier?

I am having a project in which I need to group test cases failing due to same faults, and obviously, test cases are not labeled with due-to fault. So clearly we have an unsupervised classification (...
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1answer
433 views

How to perform cross validation in semi-supervised learning

Suppose in semi-supervised learning, we have labeled set $X_L$ and unlabeled set $X_U$ Is it ok to validate model performance on labeled data only? How to do cross-validation in transductive learning,...
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1answer
2k views

Understanding semi supervised technique called mean teachers

I am trying to understand applying semi supervised learning as described in this paper. Describing the final recipe as described in this paper: Take a supervised architecture and make a copy of it. ...
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106 views

When is it okay to label data yourself? (And semi-supervised learning)

i'm pretty new to machine learning so i think this might be a realy basic question. Let's imagine the following scenario: I want to classify projects as either active or inactive. Projects can be ...
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342 views

How does Google Photos face recognition use user-provided labels?

I'm working on a toy project which I think is analogous to the problem of detecting faces and assigning names to them in Google Photos, so I've been thinking about how that process might work. From a ...
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45 views

Weakly supervised learning and missing labels for data that likely contains that label

I would like to know how to deal with data that misses a label, but is likely to contain the label in a weakly supervised setting. Weakly supervised background Since labeling is a time consuming and ...
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359 views

How can I perform semi-supervised learning for Random forest algorithm in R language?

I want to train random forest by semi-supervised learning. And I have figured out a co-training framework for that, but I need to extract each tree and corresponding bootstrapped training dataset from ...
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157 views

Using partial measurements of output variable in modeling

My question is: How can we use partially measured output data in a training set? This is vague, so I concretize it in a whimsical tale. Squirrels Have Nuts, But How Many? Setup There is a set $S$ of ...
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291 views

what's the difference between semi-supervised learning and partially supervised learning? [closed]

Isn't every semi-supervised problem also a partially supervised learning problem and vice versa?
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490 views

How to choose a method for binary classifier based on only positive and unlabelled examples?

I need to build a binary classifier with machine learning, as I fail to manually choose a combination of features to achieve minimal fraction of false positives. What is best practice for choosing a ...
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93 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 ...
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4answers
6k views

Why does using pseudo-labeling non-trivially affect the results?

I've been looking into semi-supervised learning methods, and have come across the concept of "pseudo-labeling". As I understand it, with pseudo-labeling you have a set of labeled data as well as a ...