Back to graduate school, I had been working on the so-called small sample size problem. In particular, I was working on linear discriminant analysis (LDA). For high-dimensional data (e.g. images, gene expression, etc.), the within-scatter matrix is singular when the number of samples is smaller than the dimensionality. Therefore LDA cannot be applied directly. You may think that we don’t have such small sample size problems anymore in the era of Big Data. Well, the challenge is deeper than what it looks like. Continue reading
Professor Clayton Christensen’s theory of disruptive innovation has been enjoying a huge success on examining low-end disruptions and new-market disruptions. But it had recently met difficulties to explaining high-end disruptions such as iPhone and Telsa. In fact, technologies that starts from high-end market and then reaches mainstream market are not new. Thomas Edison did it more than 100 years ago.
So did only the rich (and cow boys/girls) ride the horses after Henry Ford invented the Model T. Today, Elon Musk does it again!
Tomorrow, only few can afford driving a car when self driving cars take the mainstream market.