5/4/2018 DSP mini project that provides the description of dimension reduction method; namely Kernel Principal Component Analysis (KPCA) as data mining is concerned with finding meaningful patterns in large sets of data. It considers other techniques that are used in the application of the method namely the centering in feature space. And the numerical experiments that were performed on an iris dataset. Kernel seeks to project the set of data onto a low-dimensional subspace that captures the highest possible amount of variance in the data. Kernel PCA embeds the data into a high dimensional space, called the feature space. The project reduces the time and storage space required, improves the performance of the machine learning model and reduces the computational power and it becomes easier to visualize the data when reduced to very low dimensions such as 2D or 3D.

Machine learning code with examples in Perceptron, Logic Regression, SVMs, KNNs, PCA, LDA, KPCA, Ensemble Learning, ANNs, CNNs, and DCNN.

In this repository it presents Scikit-learn libraries for the development of Machine Learning in Python so that they can deepen or incorporate into their projects, within which I describe features of the models: PCA and IPCA, Kernels and KPCA, regularization through Lasso and Ridge , ElasticNet, outlier resolution, data preparation for robust regressions, assembly methods, and data preparation for these methods, Bagging, Boosting implementation, clustering strategies, Batch K-Means and Mean-Shhift, Cross Validation, and use K-Folds, paramedical optimization, implementation of Randomized, exit to production with API in Flask If you have any questions and / or comments, you can contact me at my email: gonzalo-a@hotmail.com Thank you

This is a repository to store code and documentation of experiments made with PCA and kPCA algorithms in python.

Machine Learning Algorithms utilizing PCA (Principal Component Analysis) and KPCA (Kernel Principal Component Analysis) to facilitate facial matching

This is the advanced version of the previously created signature verification code wherein it only used the basic Convolutional Neural Network (CNN) but here, I have used some Image processing techniques such as Gaussian Filters and Difference of Gaussian for noise reduction of the images of the signature and Grey Level Co- occurrence Matrix for feature extraction along with PCA and KPCA for further reducing the dimensions of the dataset. At the end I have used10 different Machine Learning Algorithms for training the datasets.