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Discriminant analysis methods can be good candidates to address such problems. These methods are supervised, so they include label information. The goal is to find directions on which the data is best separable. One of the very wellknown discriminant analysis method is the Linear Discriminant Analysis. Linear Discriminant Analysis (LDA) is most commonly used as dimensionality reduction technique in the pre-processing step for pattern-classification and machine learning applications. The goal is to project a dataset onto a lower-dimensional space with good class-separability in order avoid overfitting (curse of dimensionality) and also reduce computational costs. Pertaining to our problem, we are given a [32 X 32] binary image as input and the goal is to apply LDA technique to transform the features into a lower dimensional space, which maximizes the ratio of the between-class variance to the within-class variance, thereby guaranteeing maximum class separability between two classes in our case with the minimal loss.
This gives steady state Network Flow from the given Pref-Defined Network Flow of a System. And a Python implementation of Larry Page's famous PageRank algorithm.
A TypeScript matrix math library @matrixlab.ts
Linear Algebra library in GDScript for Godot Engine. Also available as C++ GDNative plugin!
Basic introduction to linear algebra applied to data analysis, implemented with a Pluto notebook
Linear Algebra,statistics and data science topics
A reimplementation of the map.m demonstration program.
:recycle: Python library to experiment with Linear Algebra concepts (work in progress)
In this repository you can find all of my assignments for Fundamentals of Programming Course when I was in 1st semester of my bachelor's at IAUSTB.
Lecture notes, assignments, and other materials for a one-semester introductory course on topics in linear algebra and computer science applications thereof.