What does "Locally Linear Embedding" mean?

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Locally Linear Embedding (LLE) is a method used to reduce the number of dimensions in data while keeping important features. The goal is to simplify complex data, making it easier to analyze and visualize.

In LLE, the method looks at small sets of points in the data. It assumes that these points can be represented by a linear combination of their neighbors. By focusing on local relationships, LLE captures the structure of the data without losing essential information.

After defining these relationships, LLE transforms the data into a lower-dimensional space. This keeps the essential characteristics of the original data while making it simpler. It is especially useful in cases where the data has many dimensions, like images or sensor readings.

LLE is commonly applied in areas where understanding the layout of data is vital, such as image processing, stress detection, and pattern recognition. By reducing dimensions, LLE helps in making complex data more manageable and insightful.