Advantages of Dimensionality Reduction

Dimensionality Reduction

Dimensionality Reduction is the method of transformation of high-dimensional data into a meaningful representation of reduced dimensionality. Ideally, the reduced representation should have a dimensionality that corresponds to the intrinsic dimensionality of the data. The intrinsic dimensionality of data is the minimum number of parameters needed to account for the observed properties of the data.

Dimensionality Reduction is important in many domains since it mitigates the curse of dimensionality and other undesired properties of high-dimensional spaces. This process must ensure that it conveys similar information concisely. These techniques are typically used while solving machine learning problems to obtain better features for a classification or regression task.

Benefits of Dimensionality Reduction

1. It helps in data compressing and reducing the storage the storage space required.

2. It fastens the time required for performing the same computations. Fewer dimensions lead to less computing. Also, fewer dimensions can allow the usage of algorithms unfit for a large number of dimensions.

3. It takes care of multi-colinearity which improves the model performance. It removes redundant features.

4. It is helpful in noise removal and as a result of that, we can improve the performance of models.

5. Reducing the dimensions of data to 2D or 3D may allow us to plot and visualize it precisely. You can then observe patterns more clearly.