Let’s say you have data points on a sheet of paper, and you want to separate them into two groups. Imagine you have a magic wand (i.e., the kernel trick) that allows you to lift the points off the paper into the air. In the air, you can easily draw a line or a curve to separate the floating points.
Now, when you’re satisfied with the separation in the air, you use the magic wand again to bring everything back down to the paper. Miraculously, the separation you drew in the air translates to a more complex decision boundary on the paper that effectively separates your original data points.
In the SVM world, this “magic wand” is the kernel trick. It allows SVMs to implicitly work in a higher-dimensional space, making it possible to find more intricate decision boundaries that weren’t achievable in the original space. The key is that you don’t have to explicitly compute the coordinates of the higher-dimensional space; the kernel trick does this for you.
In summary, the kernel trick lifts your data into a higher-dimensional space, where SVMs can find more sophisticated ways to separate different classes. It’s a powerful tool for handling complex data scenarios.
SVMs leverage the kernel trick to transform the input data into a higher-dimensional space, where a linear decision boundary can be found. The kernel function plays a crucial role in this process, mapping the input data into a feature space where the relationships between variables may be more easily separable.
The most commonly used kernel functions are the linear kernel, which represents a linear decision boundary, the polynomial kernel, which introduces non-linearity with higher-order polynomial features, and the radial basis function (RBF) kernel, which allows for a more flexible and non-linear decision boundary. The choice of the kernel function and its parameters significantly influences the SVM’s ability to model complex relationships in the data.
As we now have a basic idea about SVMs, let us next understand data augmentation, image data augmentation, and the various techniques used for this.
Data augmentation
Data augmentation is the process of creating new data points from the existing data points by applying various transformations such as rotation, translation, and scaling. Data augmentation is used to increase the size of the training dataset and improve the generalizability and accuracy of the model by helping the model to learn more features and patterns in the data.
Image data augmentation
Image data augmentation is a technique of augmenting the image dataset to improve the accuracy of the model. The following is a selection of the techniques that can be used for image data augmentation.