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Document type:
Masterarbeit
Author(s):
Keerthi Gaddameedi
Title:
Efficient and Scalable Kernel Matrix Approximation using Hierarchical Decomposition
Abstract:
Rapid expansion of data and its availability demands better and efficient ways to process, utilize, visualize and interpret it. Dimensionality reduction algorithms are used to mitigate the curse of dimensionality by extracting useful information and dis- carding the rest. But most common algorithms follow a linear approach. The intrinsic dimension of the real-world data may not always be in a linear space and hence the traditional approaches fail to produce meaningful results in such cases....     »
Supervisor:
Hans-Joachim Bungartz
Advisor:
Severin Reiz
Year:
2022
Quarter:
3. Quartal
Year / month:
2022-06
Month:
Jun
University:
Technical University of Munich
Faculty:
TUM School of Computation, Information and Technology
TUM Institution:
Fakultät für Informatik
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