Results 51 to 60 of about 201,485 (174)
Manifold learning in statistical tasks
Учёные записки Казанского университета: Серия Физико-математические науки, 2018 Many tasks of data analysis deal with high-dimensional data, and curse of dimensionality is an obstacle to the use of many methods for their solving.
A.V. Bernstein
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Review on graph learning for dimensionality reduction of hyperspectral image
Geo-spatial Information Science, 2020 Graph learning is an effective manner to analyze the intrinsic properties of data. It has been widely used in the fields of dimensionality reduction and classification for data. In this paper, we focus on the graph learning-based dimensionality reduction
Liangpei Zhang, Fulin Luo
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Applied Sciences, 2021 Manifold learning tries to find low-dimensional manifolds on high-dimensional data. It is useful to omit redundant data from input. Linear manifold learning algorithms have applicability for out-of-sample data, in which they are fast and practical ...
Ümit Öztürk, Atınç Yılmaz
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Manifold learning based on kernel density estimation
Учёные записки Казанского университета: Серия Физико-математические науки, 2018 The problem of unknown high-dimensional density estimation has been considered. It has been suggested that the support of its measure is a low-dimensional data manifold. This problem arises in many data mining tasks.
A.P. Kuleshov +2 more
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Diversity Multi-View Clustering With Subspace and NMF-Based Manifold Learning
IEEE Access, 2023 Since the complementarity information among multiple views has been exploited to improve the clustering effect significantly, multi-view clustering has become a hot topic, and many multi-view clustering methods have emerged.
Jiaman Ding +4 more
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Parametric Local Metric Learning for Nearest Neighbor Classification [PDF]
, 2012 We study the problem of learning local metrics for nearest neighbor classification. Most previous works on local metric learning learn a number of local unrelated metrics.
Kalousis, Alexandros +2 more
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Recovering manifold representations via unsupervised meta-learning
Frontiers in Computer ScienceManifold representation learning holds great promise for theoretical understanding and characterization of deep neural networks' behaviors through the lens of geometries.
Yunye Gong +6 more
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S-Isomap++: Multi Manifold Learning from Streaming Data
, 2017 Manifold learning based methods have been widely used for non-linear dimensionality reduction (NLDR). However, in many practical settings, the need to process streaming data is a challenge for such methods, owing to the high computational complexity ...
Chandola, Varun, Mahapatra, Suchismit
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Learning Smooth Pattern Transformation Manifolds [PDF]
IEEE Transactions on Image Processing, 2013 Manifold models provide low-dimensional representations that are useful for processing and analyzing data in a transformation-invariant way. In this paper, we study the problem of learning smooth pattern transformation manifolds from image sets that represent observations of geometrically transformed signals.
Vural, Elif, Frossard, Pascal
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