Solo+MIA+Model_Exporter

Solo with the Power of the Multivariate Image Analyzer and the flexibility of Model_Exporter!

Solo+MIA+Model_Exporter combines the stand-alone graphical environment of Solo with the flexibility of the model deployment options of Model Exporter and tools from our popular MIA_Toolbox. This combination allows users to apply multivariate analysis tools directly to hyperspectral image data and view the results in image format. The inclusion of Model_Exporter provides a unique and powerful means to add fast and flexible model prediction capability to any third-party software package without the need for outside libraries or toolboxes. Model_Exporter exports models into a stand-alone “predictor” script which includes a simple-to-implement formula to perform a model prediction. Add the power of multivariate modeling to your proprietary application or simply apply models in Python, Java, .NET languages (C# etc), MathWorks’ MATLAB®, Octave, or LabView®, without the need for costly 3rd party toolboxes or libraries.

Solo provides the Graphical Interfaces to quickly manage and analyze data, author and apply models and interpret results.

Key Methods Included:

Data Exploration and Pattern Recognition (Principal Components Analysis (PCA), Parallel Factor Analysis (PARAFAC), t-distributed Stochastic Neighbor Embedding (t-SNE), Uniform Manifold Approximation and Projection (UMAP), Multiway PCA…)
Classification (SIMCA, k-nearest neighbors, Partial Least Squares Discriminant Analysis (PLS-DA), Support Vector Machine Classification (SVM-DA), Artificial Neural Network Classification (ANN-DA), Deep Learning ANN Classification (ANNDL-DA)Boosted Regression and Classification Trees (XGBoost), Clustering (HCA)…)
Linear and Non-Linear Regression (Partial Least Squares (PLS), Principal Components Regression (PCR), Multiple Linear Regression (MLR), Classical Least Squares (CLS), Support Vector Machine Regression (SVM), Artificial Neural Networks (ANN), Deep Learning ANN (ANNDL), Boosted Regression and Classification Trees (XGBoost), N-way PLS, Locally Weighted Regression…)
Self-modeling Curve Resolution, Pure Variable Methods (Multivariate Curve Resolution (MCR), Purity (compare to SIMPLSMA), CODA_DW, CompareLCMS…)
Curve fitting and Distribution fitting and analysis tools
Instrument Standardization (Piece-wise Direct, Windowed Picewise, OSC, Generalized Least Squares Preprocessing, Spectral Subspace Transformation (SST)…)
Advanced Graphical Data Set Editing and Visualization Tools
Advanced Customizable Order-Specific Preprocessing (Centering, Scaling, Smoothing, Derivatizing, Transformations, Baselining, Generalized Least Squares Weighting (GLSW) and many, many more)
Missing Data Support (SVD and NIPALS)
Variable Selection (Genetic algorithms, iPLS, Selectivity Ratio, VIP…