10-601 Machine Learning (Fall 2010) Principal Component Analysis
نویسنده
چکیده
One could think of many reasons where transforming a data set at hand to a lowdimensional space might be desirable, e.g. it makes the data easier to manipulate with and requires less computational resource. It is, however, important to perform such transformations in a principled way because any kind of dimension reduction might lead to loss of information, and it is crucial that the algorithm preserves the useful part of the data while discarding the noise. Here we motivate PCA from three perspectives and explain why preserving maximal variability makes sense.
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