Principal Component Analysis (PCA) is an unsupervised learning algorithms and it is mainly used for dimensionality reduction, lossy data compression and feature extraction. It is the mostly used unsupervised learning algorithm in the field of Machine Learning. In this video tutorial, after reviewing the theoretical foundations of Principal Component Analysis (PCA), this method is implemented step-by-step in Python and MATLAB. ...

Read More »# Machine Learning

## Linear Discriminant Analysis (LDA) in MATLAB

Downloads The download link of this project follows. Implementation of Linear Discriminant Analysis (LDA) in MATLAB Download

Read More »## Time-Series Prediction using GMDH in MATLAB

Time-series prediction can be assumed as a special case of nonlinear regression and function approximation. Hence, nonlinear regression approaches, like Artificial Neural Networks and Group Method of Data Handling (GMDH) can be applied to perform time-series forecasting problems. In this post, we are going to share with you, the source codes of time-series prediction using GMDH in MATLAB, which is ...

Read More »## Feature Selection using Metaheuristics and EAs

Feature selection is one of common preprocessing tasks, which is performed to reduce the number of inputs of intelligent algorithms and models. This helps us to simplify the models, reduce the computation cost of model training, and enhance the generalization abilities of the model and prevention of over-training. For more information on feature selection concepts and methods, you can refer ...

Read More »## Group Method of Data Handling (GMDH) in MATLAB

Group Method of Data Handling (GMDH) is a family of mathematical modeling and nonlinear regression algorithms, which is originally proposed by Alexey Grigorevich Ivakhnenko, an Ukrainian scientist and mathematician, in 1968. This approach is also known as Polynomial Neural Network and can be assumed as a specific type of supervised Artificial Neural Network (ANN). In addition to modeling specifications, GMDH ...

Read More »## DBSCAN Clustering in MATLAB

Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is a density-based clustering algorithm, proposed by Martin Ester et al., 1996. The algorithm finds neighbors of data points, within a circle of radius ε, and adds them into same cluster. For any neighbor point, which its ε-neighborhood contains a predefined number of points, the cluster is expanded to contain its neighbors, ...

Read More »## FP-Growth Association Rule Mining in MATLAB

Like Apriori algorithm, FP-Growth is an association rule mining approach. The term FP in the name of this approach, is abbreviation of Frequent Pattern. FP-Growth uses a frequent pattern mining technique to build a tree of frequent patterns (FP-Tree), which can be used to extract association rules. Compared to Apriori, the FP-Growth approach is more efficient and it has better ...

Read More »## Apriori Association Rule Mining in MATLAB

Association Rule Mining is a common task in the field of Data Mining, involving the recognition of frequent patterns, usually in transactional databases. For example, discovering a rule like {bread, butter} → {milk} in a sales dataset is a result of association rule mining, and indicates that if a customer buys bread and butter, it is likely that they will buy ...

Read More »## Neural Gas and GNG Networks in MATLAB

Neural Gas network is a competitive Artificial Neural Network (ANN), very similar to Self-Organizing Map (SOM), which is proposed by Martinetz and Schulten, 1991. Neural Gas network can be used to solve unsupervised learning tasks, like clustering, dimensionality reduction, and topology learning. It has many applications in the fields of pattern recognition, data compression, speech recognition, and image segmentation. For ...

Read More »## Evolutionary Data Clustering in MATLAB

Clustering is an unsupervised machine learning task and many real world problems can be stated as and converted to this kind of problems. Clustering is grouping a set of data objects is such a way that similarity of members of a group (or cluster) is maximized and on the other hand, similarity of members in two different groups, is minimized. ...

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