Google Tech TalksJune 1, 2007ABSTRACTBig two dimensional data as photos or spreadsheets are very common in applications. Higher dimensional data as three dimensional picture, or picture with voices, or profile of a person from several angles lead to a higher dimensional data.Usually this data has a lot of redundancy, has noise and may miss the information at all in certain percentage of data. In this talk I will discuss the following general problems:* Reduction process which reduces the storage space of the data* Denoising the data.* Predicting the values of the missing dataFor the two dimensional data the singular value decomposition (SVD) of an m x n matrix emerged as a very...
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