Set of functions that can be used for interpolation of scattered data of any Dimensionality.
標(biāo)簽： Dimensionality interpolation functions scattered
上傳時(shí)間： 2013-12-09
上傳用戶：maizezhen
relevant Dimensionality estimate toolbox
標(biāo)簽： Dimensionality relevant estimate toolbox
上傳時(shí)間： 2013-12-10
上傳用戶：英雄
An Introduction to Dimensionality Reduction Using Matlab
標(biāo)簽： Dimensionality Introduction Reduction Matlab
上傳時(shí)間： 2013-12-08
上傳用戶：tb_6877751
Dimensionality Reduction for Distributed Estimation in the Infinite Dimensional Regime
標(biāo)簽： Dimensionality Dimensional Distributed Estimation
上傳時(shí)間： 2017-06-07
上傳用戶：familiarsmile
Description: S-ISOMAP is a manifold learning algorithm, which is a supervised variant of ISOMAP. Reference: X. Geng, D.-C. Zhan, and Z.-H. Zhou. Supervised nonlinear Dimensionality reduction for visualization and classification. IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics, 2005, vol.35, no.6, pp.1098-1107.
標(biāo)簽： Description supervised algorithm S-ISOMAP
上傳時(shí)間： 2015-04-10
上傳用戶：wfeel
This paper studies the problem of categorical data clustering, especially for transactional data characterized by high Dimensionality and large volume. Starting from a heuristic method of increasing the height-to-width ratio of the cluster histogram, we develop a novel algorithm – CLOPE, which is very fast and scalable, while being quite effective. We demonstrate the performance of our algorithm on two real world
標(biāo)簽： data transactional categorical clustering
上傳時(shí)間： 2015-10-24
上傳用戶：evil
Feature selection is a preprocessing technique frequently used in data mining and machine learning tasks. It can reduce Dimensionality, remove irrelevant data, increase learning accuracy, and improve results comprehensibility. FCBF is a fast correlation-based filter algorithm designed for high-dimensional data and has been shown effective in removing both irrelevant features and redundant features
標(biāo)簽： preprocessing frequently selection technique
上傳時(shí)間： 2014-01-19
上傳用戶：lindor
這是LLE的原始算法，原文的參考文獻(xiàn)是：S.T.Roweis and L.K.Saul. Nonlinear Dimensionality reduction by locally linear embedding. Science, 290, 2000.
上傳時(shí)間： 2013-12-20
上傳用戶：蟲蟲蟲蟲蟲蟲
This thesis presents a comprehensive overview of the problem of facial recognition. A survey of available facial detection algorithms as well as implementation and tests of di鏗€erent feature extraction and Dimensionality reduction methods and light normalization methods are presented.
標(biāo)簽： comprehensive recognition presents overview
上傳時(shí)間： 2017-05-05
上傳用戶：royzhangsz
Multiuser multiple-input-multiple-output (MU- MIMO) systems are known to be hindered by Dimensionality loss due to channel state information (CSI) acquisition overhead. In this paper, we investigate user-scheduling in MU-MIMO systems on account of CSI acquisition overhead, where a base station dynamically acquires user channels to avoid choking the system with CSI overhead.
標(biāo)簽： Acquisition Dynamic Channel
上傳時(shí)間： 2020-05-27
上傳用戶：shancjb
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