AI with Unsupervised Machine Learning
Unsupervised machine learning is the machine learning task of inferring a function that describes the structure of "unlabeled" data (i.e. data that has not been classified or categorized). Since the examples given to the learning algorithm are unlabeled, there is no straightforward way to evaluate the accuracy of the structure that is produced by the algorithm—one feature that distinguishes unsupervised learning from supervised learning and reinforcement learning.
Why is Unsupervised Machine Learning important?
Unsupervised machine learning purports to uncover previously unknown patterns in data, but most of the time these patterns are poor approximations of what supervised machine learning can achieve. Additionally, since you don’t know what the outcomes should be, there is no way to determine how accurate they are, making supervised machine learning more applicable to real-world problems.
The best time to use unsupervised machine learning is when you don’t have data on desired outcomes, like determining a target market for an entirely new product that your business has never sold before. However, if you are trying to get a better understanding of your existing consumer base, supervised learning is the optimal technique.
Some applications of unsupervised machine learning techniques include:
| Applications | |
|---|---|
| Clustering | allows you to automatically split the dataset into groups according to similarity. Often, however, cluster analysis overestimates the similarity between groups and doesn’t treat data points as individuals. For this reason, cluster analysis is a poor choice for applications like customer segmentation and targeting. |
| Anomaly | detection can automatically discover unusual data points in your dataset. This is useful in pinpointing fraudulent transactions, discovering faulty pieces of hardware, or identifying an outlier caused by a human error during data entry. |
| Association mining | identifies sets of items that frequently occur together in your dataset. Retailers often use it for basket analysis, because it allows analysts to discover goods often purchased at the same time and develop more effective marketing and merchandising strategies. |
| Latent variable models | are commonly used for data preprocessing, such as reducing the number of features in a dataset (dimensionality reduction) or decomposing the dataset into multiple components. |
The patterns you uncover with unsupervised machine learning methods may also come in handy when implementing supervised machine learning methods later on. For example, you might use an unsupervised technique to perform cluster analysis on the data, then use the cluster that each row belongs to as an extra feature in the supervised learning model (see semi-supervised machine learning). Another example is a fraud detection model that uses anomaly detection scores as an extra feature.
How does unsupervised machine learning work?
Unsupervised machine learning works by analyzing the data without its labels for the hidden structures within it, and through determining the correlations, and for features that actually correlate to data items. It is being used for clustering, dimensionality reduction, feature learning, density estimation, etc.
The hidden structure sometimes called feature vector, represents the input data such a way that if the same feature vector is being used to reconstruct the input, then one can do that with some acceptable loss. The variance in two feature vectors of two inputs directly proportional to the variance in the inputs itself. Thus this hidden structure or feature vector only represents features in the data that actually give distinction to it.
RBM, autoencoders are the two simple form of unsupervised neural networks. Moreover a CNN network without a FC network can be used as a encoder for the images. The training for such encoder networks are done by using a decoder network, and optimizing by reducing the reconstruction loss.
A central application of unsupervised learning is in the field of density estimation in statistics, though unsupervised learning encompasses many other problems (and solutions) involving summarizing and explaining various key features of data.
Unsupervised Machine Learning Advantages
Algorithms used in unsupervised learning vary, including:
| Approaches |
|---|
| Clustering |
| k-means |
| mixture models |
| hierarchical clustering, |
| Anomaly detection |
| Neural Networks |
| Autoencoders |
| Deep Belief Nets |
| Hebbian Learning |
| Generative Adversarial Networks |
| Self-organizing map |
| Approaches for learning latent variable models such as |
| Expectation–maximization algorithm (EM) |
| Method of moments |
| Blind signal separation techniques, e.g., |
| Principal component analysis, |
| Independent component analysis, |
| Non-negative matrix factorization, |
| Singular value decomposition. |
| In neural networks |
The classical example of unsupervised learning in the study of both natural and artificial neural networks is subsumed by Donald Hebb's principle, that is, neurons that fire together wire together. In Hebbian learning, the connection is reinforced irrespective of an error, but is exclusively a function of the coincidence between action potentials between the two neurons. A similar version that modifies synaptic weights takes into account the time between the action potentials (spike-timing-dependent plasticity or STDP). Hebbian Learning has been hypothesized to underlie a range of cognitive functions, such as pattern recognition and experiential learning.
Among neural network models, the self-organizing map (SOM) and adaptive resonance theory (ART) are commonly used in unsupervised learning algorithms. The SOM is a topographic organization in which nearby locations in the map represent inputs with similar properties. The ART model allows the number of clusters to vary with problem size and lets the user control the degree of similarity between members of the same clusters by means of a user-defined constant called the vigilance parameter. ART networks are also used for many pattern recognition tasks, such as automatic target recognition and seismic signal processing. The first version of ART was "ART1", developed by Carpenter and Grossberg (1988).
Method of moments
One of the statistical approaches for unsupervised learning is the method of moments. In the method of moments, the unknown parameters (of interest) in the model are related to the moments of one or more random variables, and thus, these unknown parameters can be estimated given the moments. The moments are usually estimated from samples empirically. The basic moments are first and second order moments. For a random vector, the first order moment is the mean vector, and the second order moment is the covariance matrix (when the mean is zero). Higher order moments are usually represented using tensors which are the generalization of matrices to higher orders as multi-dimensional arrays.
In particular, the method of moments is shown to be effective in learning the parameters of latent variable models.
The Expectation–maximization algorithm (EM) is also one of the most practical methods for learning latent variable models. However, it can get stuck in local optima, and it is not guaranteed that the algorithm will converge to the true unknown parameters of the model. In contrast, for the method of moments, the global convergence is guaranteed under some conditions.
Unsupervised Machine Learning Examples
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Behavioral-based detection in network security has become a good application area for a combination of supervised and unsupervised machine learning. This is because the amount of data for a human security analyst to analyze is impossible (measured in terabytes per day) to review to find patterns and anomalies. According to Giora Engel, co-founder of LightCyber, in a Dark Reading article, "The great promise machine learning holds for the security industry is its ability to detect advanced and unknown attacks—particularly those leading to data breaches."
Unsupervised Machine Learning AI Jobs
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| Unsupervised Machine Learning AI Jobs | |
|---|---|
| INDEED | Indeed Unsupervised Machine Learning AI Jobs |
| Monster | Monster Unsupervised Machine Learning AI Jobs |
Unsupervised Machine Learning AI Videos
These are some videos related to Unsupervised Machine Learning and artificial intelligence. Enjoy!
More information: Unsupervised Machine Learning offers a huge list of benefits to all. The usage of Unsupervised Machine Learning is such that it cannot be limited to only one activity. Its growing popularity has allowed it to enter into some of the most popular and complex processes like Artificial Intelligence (AI), Machine Learning (ML), natural language processing, data science etc. We hope this page was helpful and provided you with some information about AI with Unsupervised Machine Learning. Check out our main page for more components of artificial intelligence resources.
