A Novel Approach for Intrinsic Dimension Estimation

• URL: http://arxiv.org/abs/2503.09485v1
• Date: Wed, 12 Mar 2025 15:42:39 GMT
• Title: A Novel Approach for Intrinsic Dimension Estimation
• Authors: Kadir Özçoban, Murat Manguoğlu, Emrullah Fatih Yetkin,
• Abstract summary: The real-life data have a complex and non-linear structure due to their nature.<n>Finding the nearly optimal representation of the dataset in a lower-dimensional space offers an applicable mechanism for improving the success of machine learning tasks.<n>We propose a highly efficient and robust intrinsic dimension estimation approach.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The real-life data have a complex and non-linear structure due to their nature. These non-linearities and the large number of features can usually cause problems such as the empty-space phenomenon and the well-known curse of dimensionality. Finding the nearly optimal representation of the dataset in a lower-dimensional space (i.e. dimensionality reduction) offers an applicable mechanism for improving the success of machine learning tasks. However, estimating the required data dimension for the nearly optimal representation (intrinsic dimension) can be very costly, particularly if one deals with big data. We propose a highly efficient and robust intrinsic dimension estimation approach that only relies on matrix-vector products for dimensionality reduction methods. An experimental study is also conducted to compare the performance of proposed method with state of the art approaches.

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