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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Looking for a way to train a model to learn optimal parameters/hyperparameters of clustering
I have 5000 docs, each is a review. For each review, I'm plotting the sentences in a semantic dimension. Now, I'm applying clustering to these points for each review. The success of my model depends ...
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Best way to classify text based on a set of seed/keywords
This question was asked six years ago but figured it was worth an update.
I'm attempting to filter news data based on a set of keywords. I think the ideal process for this would be some form of:
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Linear Discriminant Analysis with unlabeled data
In section 4.4.5 "Logistic regression or LDA?" of Elements of Statistical Learning by Friedman, Tibshirani and Hastie, it is claimed the following:
From the mixture formulation [that is, ...
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What are the general steps of a semi-supervised setting?
Let's consider I have 1000 unlabeled data points. Out of these 1000 points, I manually labeled 200. Now, I am feeling lazy and don't want to label manually the remaining 800 data points.
What would be ...
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What is the value of semi-supervised learning?
I've read that semi-supervised learning can be useful when you only have a small amount of labeled data. However, I'm struggling to understand the practical implications and benefits of this approach, ...
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Inference on Unlabeled Data in Semi-Supervised Learning and Determining Dataset Size
In semi-supervised learning, when a portion of unlabelled data is combined with labelled data during the training process, I'm wondering how one can perform inference specifically on the unlabelled ...
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Semi-supervised learning with two models
Let's say you want to train a model so that you can make some predictions when you get some future data. You find some training data. Some of the training records have labels but other records do not. ...
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Binary classification of nodes in a social network with weak supervision
Dislaimer: I'm a newbie in machine learning so I apologize in advance if I made any mistakes :p
Scenario
Let's say we have a social network with users and posts made by the users. A user can interact ...
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Training with unlabeled data and the probability of correct classification
Suppose we have two binary classifiers based on deep learning. The second classifier is able to tell me with a probability not very high but better than a random guess (let's say 70%), if the ...
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Do you use the PC1, PC2, PC3 or do you use PCA for feature selection in supervised learning? [closed]
My goal is to understand what is PCA for in supervised learning.
Do we use the PC1, PC2, PC3 to the supervised learning?
Do we use the generated labels to the supervised learning?
If we use the PC1, ...
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Approaches for semi-supervised fine-tuning after self-supervised pre-training
My understanding is that self-supervised learning approaches approximately work like the following (I have Wav2Vec 2 in my mind here, used in speech recognition, but NLP transformer models are similar)...
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For semi-supervised learning, is more pseudolabels always better than less pseudolabels?
Let's say I have a labeled dataset $L$ and unlabeled dataset $U$, where $U \gg L$. Suppose I focus on a subset of $U$ called $u$ and generate a subset of $u$ I'll call $u_L$ that consists of ...
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Why use IsolationForest over other supervised methods for semi supervised learning?
I have a dataset with labels that I'm using to explore unsupervised learning (IsolationForest) with. IsolationForest has a few hyperparameters, and some can be heuristically determined like maybe you ...
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Role of auxiliary objective in semi-supervised VAEs?
In these two papers, mainly:
Klys, Jack, Jake Snell, and Richard Zemel. "Learning latent subspaces in variational autoencoders." Advances in neural information processing systems 31 (2018).
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Is the likelihood for Gaussian mixture models still multimodal when Y is partially observed?
In discussing Gaussian mixture models (GMMs), https://normaldeviate.wordpress.com/2012/08/04/mixture-models-the-twilight-zone-of-statistics/ highlights the issue of
Multimodality of the Likelihood. ...
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Weak supervision vs semi-supervised learning
What exactly is the difference between these two and when should they be used?
Context : I have a large set of unlabelled data. I can get a number of weak labels / labeling functions for all of the ...
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What kind of semi-supervised learning method should be used for a low quality data set?
Consider a binary classification problem, there are $1000$ samples in the data set, of which $500$ positive and negative samples each. Positive samples have the label $1$ and negative samples have the ...
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About the mean-teacher algorithm, in the end we should use the student or the teacher model?
It may sound silly, but I didn't find an official approach about which final model I should choose when the mean-teacher training ends, even on the original paper.
I know both will have very similar ...
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Semi-supervised learning: Classification vs Clustering
In the context of a semi-supervised learning problem, what's the difference between using a classification algorithm vs a clustering algorithm?
Traditionally classification is supervised and ...
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What is the best approach: Labeled training data and unlabeled test data [closed]
I'm new into the data science world and I am working on improving my knowledge so here is my problem:
I want to build a binary classifier with the following constraints:
I have 2 files training.csv ...
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Computing a prior from two components in Naive Bayes
Given a model parameter $\theta$ that is composed of two distributions in a Naive Bayes classifier, how is $P(\theta)$ typically computed in practice?
More specifically, from the article of Nigam et ...
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Improving synthetic oversampling with unlabelled data
I am working on a classification problem with a small amount of labelled data (~200 instances) and a larger sample of unlabelled data (~500 instances).
To increase the size of the training data I am ...
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Binary Classification with a third 'uncertain' class label
Consider the task of classifying an image into two classes:
Image shows a cat;
Image shows no cat.
A data set is provided for training/testing a binary classifier. However, three labels are provided ...
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Semi-supervised classification objective from Kingma et al
In this 2014 paper, Kingma et al. develop different methods to do semi-supervised learning with VAEs. In one of their proposed solutions ("M2"), they approach this problem by incorporating ...
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Can I construct a target variable out of correlates and proxies when my training data does not have the actual target variable that I need?
I want to classify customers who at risk to churn (unsubscribe). The typical path would be to have a training set of historical data that includes observations of customers who churned so that we have ...
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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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