Does SVM suffer from curse of dimensionality?

1 Answer. SVM also suffers the problems coming from high dimensionality, but under typical settings to a lesser degree compared to (say) LDA. I can imagine SVM would only have to take dot products of support vectors and a feature vector to classify a new data point, so it might not suffer from curse of dimensionality.

Does SVM suffer from overfitting?

SVMs avoid overfitting by choosing a specific hyperplane among the many that can separate the data in the feature space. SVMs find the maximum margin hyperplane, the hyperplane that maximixes the minimum distance from the hyperplane to the closest training point (see Figure 2).

Is SVM used for dimensionality reduction?

Hyperspectral images are nonlinear and are of high dimension. Motivated by this an algorithm is introduced that classifies an image along with dimensionality reduction, using SVM. This algorithm implements dimensionality reduction and classification in a unified framework.

Why SVM works on high dimensional problems?

SVM. SVMs are well known for their effectiveness in high dimensional spaces, where the number of features is greater than the number of observations. The model complexity is of O(n-features * n² samples) so it’s perfect for working with data where the number of features is bigger than the number of samples.

Does Random Forest suffer from curse of dimensionality?

The random forest has a lower model variance than an ordinary individual tree. Immunity to the curse of Dimensionality: Since each tree does not consider all the features, the feature space (the number of features a model has to consider) reduces. This makes the algorithm immune to the curse of dimensionality.

How does SVM prevent overfitting?

In SVM, to avoid overfitting, we choose a Soft Margin, instead of a Hard one i.e. we let some data points enter our margin intentionally (but we still penalize it) so that our classifier don’t overfit on our training sample. The higher the gamma, the higher the hyperplane tries to match the training data.

Why is SVM less prone to overfitting?

3 Answers. In practice, the reason that SVMs tend to be resistant to over-fitting, even in cases where the number of attributes is greater than the number of observations, is that it uses regularization.

Why is SVM better than LDA?

SVM makes no assumptions about the data at all, meaning it is a very flexible method. The flexibility on the other hand often makes it more difficult to interpret the results from a SVM classifier, compared to LDA. SVM classification is an optimization problem, LDA has an analytical solution.

Is it beneficial to perform dimensionality reduction before fitting an SVM Why or why not?

It is beneficial to perform dimensionality reduction before fitting an SVM if the number of features is large when compared to the number of observations. Works well with even unstructured and semi structured data set. It works effectively even if the number of features are greater than the number of samples.

Why SVM is effective in cases where the number of dimensions is greater than the number of samples?

It is effective in cases where the number of dimensions is greater than the number of samples. It uses a subset of training points in the decision function (called support vectors), so it is also memory efficient.