Unsupervised Learning: Get an Insight into Top 3 Learning Techniques in Deep Learning
This entry was posted in Dissertation , Editor , Research Paper , on September 28 , 2019.
Deep Learning is gaining popularity among Artificial Intelligence (AI) and data scientist community. With growing time, it has become of the love of data lovers across the globe. However, of late, researchers have begun to question if deep learning is the future of Artificial Intelligence. Today, although supervised learning (a deep learning technique) has an upper hand in the industry, it is unsupervised learning that enables the researcher to find the unknown trends and design in the data. The main goal of unsupervised learning is to allow the model and to discover insights & patterns on its own. Unsupervised learning recognises commonalities in the underlying data and reacts on the basis of the presence of commonalities in the dataset. For example, let’s consider a baby and its pet cat. In a few weeks, the baby identifies all the features two eyes, ears nose and play with the cat. A few days later a family friend visit and gets his cat and the baby was able to recognize that this pet is similar to his pet due to similar features but knew that cat was different from his cat. This is a clear example that it learns and discover the structure by itself. With that said, unsupervised learning are of 3 types. Here’s a detailed look at each of them. Clustering involves searching for the pattern and trends of categorized data. These algorithms process the data and find clusters or groups of same features. Groups of data are formed by using a clustering algorithm so as to classify the data in specific groups. All the data points have the same properties, and features in the same group, whereas different groups have unique properties or characteristics.The popular types of clustering we use are:
Clustering techniques are an easy yet powerful technique as they require intensive effort to give us a very relevant insight from our data. In that capacity, it’s been utilized in numerous applications for a considerable length of time including, for hereditary and species gathering, therapeutic imaging, statistical surveying, for understanding the multiple collections of organizations, clients dependent on individual traits, etc.In Machine Learning (ML), there includes certain conditions where feature representations or image that are just too huge to handle. Autoenodes is a technique in which we use neural networks for the same.For instance, consider you are using face recognition application and would like to save all the templates of a person’s face in your data warehouse for future reference. To keep the colour image of the person of 168×168, we would have to keep 168 * 168 * 3 float values for each face! Here the storage space required will be huge. This is when autoencoders come into the picture. With an autoencoder, we can encode features which takes less space, while representing the same data.However to work this out, it is a must to train a neural network to predict its input. While doing so, note that the middle layer of the autoencoder will have fewer features than input and output.
- K-Means Clustering – clustering your data points within a k-number of mutually exclusive clusters. A lot of complexity encompasses how to select the right number (K).
- Hierarchical Clustering – a technique used to cluster the data points into the origin and minor clusters. You might split your consumers between fresher and more experienced ones, and then divide all of these groups into individual clusters.
- Probabilistic Clustering – a technique that is used to cluster all the data points using a probabilistic scale.
Expectation-Maximization (EM) algorithms have a set of iterative approaches meant to determine the parameters of various statistical models to explicitly model data. For example, consider a Gaussian distributed data and you require to find the best parameters for a Gaussian to model it. An Expectation-Maximization algorithm enables you to automatically determine the Gaussian parameters like mean and variation in every directionIn an EM algorithm, we shift within the Expectations(E) step and a Maximization (M) step. The E step uses the current parameters to develop a statistical model which can be applied to your data.Although it is quite challenging to implement unsupervised learning techniques, doing so can do wonders. Some of the benefits of unsupervised learning techniques are:
- Expectations-Maximization algorithms
Though unsupervised learning has several advantages, it includes a few drawbacks such as lack of accountability in AI, plenty of data lakes, etc. Thus even with the recent success of this process, it isn’t recommended by industry experts. However, we can expect it to be omnipresent in the near future.
- Unsupervised learning algorithm helps to find all kinds of trends and unknown pattern in the underlying data.
- The algorithms help to discover all kinds of features that could be useful for categorization.
- The input data can be analyzed and labelled in real-time in presence of learners.
- It is always easy to get unstructured and unlabeled data than the labelled as it needs manual intervention.