Unsupervised Learning Algorithms
Machine learning has seen a lot of development in recent years, and unsupervised learning is a part of that. Machine learning is a broad subject, and that’s why it’s divided into three categories. Out of those three, we’ll be discussing unsupervised learning in this article. Unsupervised learning is one of the relatively new topics in the tech sector.
It has plenty of challenges but with a vast list of advantages as well. In this article, you’ll find out what unsupervised learning is, how does it work, what its problems are, its advantages, and what are the algorithms present in it. We’ve kept it as comprehensive as possible.
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So, let’s get started.
What is Unsupervised Learning?
When you don’t give any labels to the learning algorithm and let it find structure in the input by itself, it’s called unsupervised learning. Unsupervised learning is one of three machine learning types; the other two are semi-supervised learning and supervised learning. Unsupervised learning can be a means towards an end or a goal in itself.
To understand unsupervised learning, imagine it as a test where the examiner doesn’t have an answer key to compare your answers with. What an exciting test would that be, right? Well, unsupervised learning enables you to work with the input and find the answers you were looking for. Maybe you wanted to find a pattern in the input you hadn’t noticed before. Or perhaps you want to understand how the data is distributed in a specific space.
Problems of Unsupervised Learning
Unsupervised learning might be quite popular, but that doesn’t mean it doesn’t have its problems. There are multiple challenges you can face due to these algorithms. Firstly, you can’t figure out whether you’re completing the task or not when you’re using unsupervised learning.
That’s because, in supervised learning, you have a standard to compare your output with. You define metrics that enable decision making on the basis of model tuning. Recall, precision, and other similar measures help you see how accurate your model is. And you can tweak the parameters of that model to enhance the accuracy of the same. If your accuracy weren’t high, you’d get a score accordingly, which would mean that you need to improve your model.
Unsupervised learning doesn’t have any labels. So, it is nearly impossible to get an objective measure of your model’s accuracy. How can you be sure that your k-means clustering algorithm found the right cluster? How would you determine the accuracy of its output? Supervised learning provides you with accuracy scores to help you determine whether your output is correct or not. But with unsupervised learning, you don’t have that luxury. Learn more about the types of supervised learning.
Now, whether unsupervised learning is useful for solving a problem or not depends on a lot of factors. Unsupervised learning wouldn’t be so prevalent if it didn’t have any applications. We’ve discussed its importance in the next section.
Why Unsupervised Learning is Necessary
After reading the challenges, this method poses, you might wonder if it’s even useful. Well, unsupervised learning has many benefits, and some of the reasons why it’s so prevalent are below:
- It enables machines to solve problems that human minds can’t due to bias or capacity.
- Unsupervised learning is suitable for exploring unknown data. If you don’t know what you need to find, then this is the perfect method for you.
- It’s quite costly to annotate large datasets. As a result, experts rely on a few examples to work on the problem.
- If you don’t know how many classes the data has, you’d need to use unsupervised learning algorithms. A great example of this is data mining.
A great unsupervised learning example is recommendation systems. Recommendation systems work through collecting the historical data of a person and suggesting their recommendations accordingly. These recommendation systems use unsupervised learning to make such suggestions. Examples of these systems include Netflix and YouTube.
So, you can see that unsupervised learning is quite effective for solving a specific kind of problem. Now that you recognize its importance, we can move onto more detailed sections and take a look at its categories.
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Categories of Unsupervised Learning
We can classify unsupervised learning in two categories:
When you assume a parametric distribution of data, you will use these unsupervised learning algorithms. In this case, you think that the mean and standard deviation parameterize all the members of a typical family of distributions. You also assume that the data originates from a population following a probability distribution that’s based on a specific set of parameters.
This means you can know the probability of future observations by merely knowing the mean and standard deviation. You will use the expectation-maximization algorithm and construction of Gaussian Mixture Models to predict the class of the sample you have. As you have answer labels to work with, it is a little trickier and more challenging to solve such problems. You wouldn’t have any corrective measures to compare your results with.
In this category, you group the data in clusters. Each cluster of the data points out something about the classes and types of the same. It’s a standard method to model and analyze data when you have small samples. With non-parametric models, you don’t have to make any assumptions about the population distribution of the data. That’s why another popular name for non-parametric unsupervised learning is distribution-free unsupervised learning.
Essential Concepts in Unsupervised Learning Algorithms
Due to high storage costs and the limitations of our computing power, we’re continually looking for ways to enhance the efficiency of our data operations. And a great solution in this regard is dimensionality reduction. Dimensionality reduction is a process present in unsupervised learning, and it works based on various concepts similar to Information Theory.
Dimensionality reduction assumes that most of the data is redundant and that you can represent almost all of the information in a data set by using just a fraction of the data you have.
Two of the most popular algorithms experts use for this purpose are Singular-Value Decomposition and Principal Component Analysis. The former factorizes your data in the product three other while the latter finds the linear combinations that convey most of the variance or difference present in your data. There are plenty of different algorithms present in unsupervised learning which perform a variety of tasks.
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By reducing the dimensionality of your data, you can enhance the machine learning pipeline. If you can reduce the data by order of magnitude, you’ll be able to reduce the required computing power and storage space substantially. This will help you in reducing the operating costs as well. A great unsupervised learning example, in this case, is computer vision. SVD and PCA are quite useful in data compression of images. And experts use one of them in the preprocessing stage of machine learning pipelines.
In clustering, you organize the data points in groups in such a way that the members of a group are similar in some fashion. It’s probably the most crucial problem present in unsupervised learning. In clustering, you create groups of data points that are similar and separate them from data points that are dissimilar to them.
Clustering focuses on determining the internal grouping of the input. As it’s a concept of unsupervised learning, it works with unlabeled data. It forms groups of data points according to the similarity it notices in their features. However, whether a cluster is correct or not depends on the user.
Clustering algorithms are of four kinds, and they are as follows:
- Probabilistic clustering algorithms
- Hierarchical clustering algorithms
- Overlapping clustering algorithms
- Exclusive clustering algorithms
The name of the first kind is self-explanatory. The second one focuses on the union of two nearest clusters, while the overlapping algorithms use fuzzy sets so that a point might belong to multiple clusters. The last one group’s data in such a way that a data point of one cluster couldn’t belong to other groups.
In generative models, you get the training data to generate new samples from it. Such models have the task of creating data similar to the one you give to them. And they do so through learning the essence of their data efficiently. Generative models can learn the features of the data you provide to them, and that’s a significant long-term advantage. Image datasets are a great example of generative models. With the help of an image dataset, you can produce many similar images.
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What Next ?
Unsupervised learning is a broad concept of machine learning. There are many algorithms present in this category, and you must’ve noticed how much variety is present among them. If you want to learn more about this topic, you should head to our blog. You’ll find plenty of useful articles on unsupervised learning and machine learning.
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