"Non UBC"@en . "DSpace"@en . "Espig, Mike"@en . "2016-07-27T05:02:46Z"@* . "2016-01-26T16:16"@en . "The coming century is surely the century of high dimensional data. With the rapid growth of computational chemistry and distributed parameter systems, high-dimensional data becomes very common. Thus, analyzing high dimensional data is an urgent problem of great practical importance. However, there are some unique challenges for analyzing data of high dimensions, including (1) the curse of dimensionality and (2) the meaningfulness of the similarity measure in the high dimension space. With standard techniques it is impossible to store all entries of the high-dimensional data\nexplicitly. The reason is that the computational complexity and the storage cost are growing exponentially with the number of dimensions. Besides of the storage one should also solve this\nhigh-dimensional problems in a reasonable (e.g. linear) time and obtain a solution in some compressed (low-rank/sparse) tensor formats. The complexity of many existing algorithms\nis exponential with respect to the number of dimensions. With increasing dimensionality, these algorithms soon become computationally intractable and therefore inapplicable in many real\napplications. During the last years, tensor format representation techniques were successfully applied to high-dimensional problems. In our talk, we show how these low-rank approximations can be computed, stored and manipulated with minimal effort."@en . "https://circle.library.ubc.ca/rest/handle/2429/58560?expand=metadata"@en . "32 minutes"@en . "video/mp4"@en . ""@en . "Author affiliation: RWTH Aachen University"@en . "10.14288/1.0307155"@en . "eng"@en . "Unreviewed"@en . "Vancouver : University of British Columbia Library"@en . "Banff International Research Station for Mathematical Innovation and Discovery"@en . "Attribution-NonCommercial-NoDerivatives 4.0 International"@en . "http://creativecommons.org/licenses/by-nc-nd/4.0/"@en . "Faculty"@en . "BIRS Workshop Lecture Videos (Banff, Alta)"@en . "Mathematics"@en . "Quantum theory"@en . "Partial differential equations"@en . "Applied computer science"@en . "The numerical treatment of high dimensional problems by means of tensor format representations"@en . "Moving Image"@en . "http://hdl.handle.net/2429/58560"@en .