SparsePCA Projection Example in Python

     Sparse Principal Component Analysis is a type of PCA analysis method. SparsePCA extracts sparse components to build the data.

    The Scikit-learn API provides SparsePCA class to apply Sparse PCA method in Python. In this tutorial, we'll briefly learn how to project data by using SparsePCA and visualize the projected data in Python. The tutorials covers:

  1. Iris dataset SparsePCA projection and visualizing
  2. MNIST dataset SparsePCA projection and visualizing
  3. Source code listing

Anomaly Detection Example With OPTICS Method in Python

    Ordering Points To Identify the Clustering Structure (OPTICS) is an algorithm that estimates density-based clustering structure of a given data. It applies the clustering method similar to DBSCAN algorithm.

    In this tutorial, we'll learn how to apply OPTICS method to detect anomalies in given data. Here, we use OPTIC class of Scikit-learn API. The tutorial covers:

  1. Preparing the data
  2. Anomaly detection with OPTICS
  3. Source code listing

Spectral Clustering Example in Python

    Spectral clustering is a technique to apply the spectrum of the similarity matrix of the data in dimensionality reduction. It is useful and easy to implement clustering method.  

    The Scikit-learn API provides SpectralClustering class to implement spectral clustering method in Python. The SpectralClustering applies the clustering to a projection of the normalized Laplacian. In this tutorial, we'll briefly learn how to cluster and visualize data with SpectralClustering in Python. The tutorial covers:

  1. Preparing the data
  2. Clustering with the SpectralClustering and visualizing
  3. Source code listing

TSNE Visualization Example in Python

     T-distributed Stochastic Neighbor Embedding (T-SNE) is a tool for visualizing high-dimensional data. T-SNE, based on stochastic neighbor embedding, is a nonlinear dimensionality reduction technique to visualize data in a two or three dimensional space.

    The Scikit-learn API provides TSNE class to visualize data with T-SNE method. In this tutorial, we'll briefly learn how to fit and visualize data with TSNE in Python. The tutorials covers:

  1. Iris dataset TSNE fitting and visualizing
  2. MNIST dataset TSNE fitting and visualizing
  3. Source code listing

Dimensionality Reduction with Sparse, Gaussian Random Projection and PCA in Python

    Dimensionality reducing is used when we deal with large datasets, which contain too many feature data, to increase the calculation speed, to reduce the model size, and to visualize the huge datasets in a better way. The purpose of this method is to keep the most important data while removing the most of the feature data. 

    In this to tutorial, we'll briefly learn how to reduce data dimensions with Sparse and Gaussian random projection and PCA methods in Python. The Scikit-learn API provides the SparseRandomProjection, GaussianRandomProjection classes and PCA transformer function to reduce data dimension. After reading this tutorial, you'll learn how to reduce dimensionality of the dataset by using those methods. The tutorial covers:

  1. Preparing the data
  2. Gaussian random projection
  3. Sparse random projection
  4. PCA projection
  5. MNIST data projection
  6. Source code listing

Curve Fitting Example With Nonlinear Least Squares in R

    The Nonlinear Least Squares (NLS) estimate the parameters of a nonlinear model. R provides 'nls' function to fit the nonlinear data. The 'nls' tries to find out the best parameters of a given function by iterating the variables. 

    In this tutorial, we'll briefly learn how to fit nonlinear data by using the 'nls' function in R. The 'nls' comes in a 'stats' base package. The tutorial covers:
  1. Preparing the data
  2. Fitting the model and prediction
  3. Source code listing