Why MDS is better than other dimensionality reduction methods
- Distance: MDS clearly shows the distance or difference between data points. This is especially true when relationships between data points are important for analysis or visualization.
- Distance measurements: MDS is versatile and can handle a variety of distance measurements, including non-Euclidean distances, making it suitable for a wide range of data. Other methods, such as SNE or autoencoders, are easier to explain. Coordinates correspond directly to data points in low-level space.
- Solid mathematical foundation: MDS has a good mathematical foundation, making it useful and interpretable.
- History: MDS has a long history and is widely used in fields such as psychology, geography, and social sciences, making it popular and reliable.
Sklearn | Multi-dimensional Scaling (MDS) Python Implementation from Scratch
Scikit-learn (sklearn) is a Python machine-learning package that is open-source and free to use. It is Python’s most popular machine-learning library, and it is extensively used in business and academics. Scikit-learn includes a wide range of machine learning methods, including supervised learning (classification, regression), unsupervised learning (clustering, dimensionality reduction), model selection and evaluation, data preparation, and feature engineering. In this article, we will discuss an unsupervised learning technique that is commonly used to visualize the relationships between data points in a high-dimensional space by mapping them to a lower-dimensional space, such as 2D or 3D, while preserving the pairwise distances between the data points as much as possible.
Table of Content
- Multi-dimensional Scaling (MDS)
- Why is Multi-dimensional Scaling (MDS) important?
- Application of MDS
- Advanced Multi-dimensional Scaling (MDS)
- Limitations of MDS
- Mathematical Formulation of MDS
- Why MDS is better than other dimensionality reduction methods
- MDS on Digits Dataset
- MDS on Make_blobs dataset