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Backdoors on Manifold Learning [PDF]
Proceedings of the 2024 ACM Workshop on Wireless Security and Machine LearningRecently, attackers have targeted machine learning systems, introducing various attacks. The backdoor attack is popular in this field and is usually realized through data poisoning.
Tajalli, Behrad +4 more
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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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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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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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Manifold aligned density estimation [PDF]
, 2010 With the advent of the information technology, the amount of data we are facing today is growing in both the scale and the dimensionality dramatically. It thus raises new challenges for some traditional machine learning tasks.
Wang, Xiaoxia
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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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Incremental Unsupervised-Learning of Appearance Manifold with View-Dependent Covariance Matrix for Face Recognition from Video Sequences [PDF]
, 2009 We propose an appearance manifold with view-dependent covariance matrix for face recognition from video sequences in two learning frameworks: the supervised-learning and the incremental unsupervised-learning. The advantages of this method are, first, the
MURASE, Hiroshi +3 more
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Enhancing cluster analysis via topological manifold learning [PDF]
We discuss topological aspects of cluster analysis and show that inferring the topological structure of a dataset before clustering it can considerably enhance cluster detection: we show that clustering embedding vectors representing the inherent ...
Scheipl, Fabian +3 more
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Semi-Supervised Manifold Alignment Using Parallel Deep Autoencoders
Algorithms, 2019 The aim of manifold learning is to extract low-dimensional manifolds from high-dimensional data. Manifold alignment is a variant of manifold learning that uses two or more datasets that are assumed to represent different high-dimensional representations ...
Fayeem Aziz +2 more
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