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SVM Kernel Trick

Hard · Linear Algebra & Machine Learning · Quant Trader interview question · SVM, Kernel Trick, Machine Learning, Non-linear Classification

An algorithmic trading firm is developing a trading strategy using Support Vector Machines (SVMs). They need to classify market regimes as either "high volatility" or "low volatility" based on several technical indicators. The initial linear SVM performs poorly, suggesting a non-linear relationship. They decide to use a kernel SVM to capture the non-linearity, but are concerned about the computational cost of explicitly mapping the data to a higher-dimensional feature space. Explain the kernel