210 research outputs found
HyperNTF: A Hypergraph Regularized Nonnegative Tensor Factorization for Dimensionality Reduction
Most methods for dimensionality reduction are based on either tensor
representation or local geometry learning. However, the tensor-based methods
severely rely on the assumption of global and multilinear structures in
high-dimensional data; and the manifold learning methods suffer from the
out-of-sample problem. In this paper, bridging the tensor decomposition and
manifold learning, we propose a novel method, called Hypergraph Regularized
Nonnegative Tensor Factorization (HyperNTF). HyperNTF can preserve
nonnegativity in tensor factorization, and uncover the higher-order
relationship among the nearest neighborhoods. Clustering analysis with HyperNTF
has low computation and storage costs. The experiments on four synthetic data
show a desirable property of hypergraph in uncovering the high-order
correlation to unfold the curved manifolds. Moreover, the numerical experiments
on six real datasets suggest that HyperNTF robustly outperforms
state-of-the-art algorithms in clustering analysis.Comment: 12 pages, 6 figures, 9 table
Interpretable Hyperspectral AI: When Non-Convex Modeling meets Hyperspectral Remote Sensing
Hyperspectral imaging, also known as image spectrometry, is a landmark
technique in geoscience and remote sensing (RS). In the past decade, enormous
efforts have been made to process and analyze these hyperspectral (HS) products
mainly by means of seasoned experts. However, with the ever-growing volume of
data, the bulk of costs in manpower and material resources poses new challenges
on reducing the burden of manual labor and improving efficiency. For this
reason, it is, therefore, urgent to develop more intelligent and automatic
approaches for various HS RS applications. Machine learning (ML) tools with
convex optimization have successfully undertaken the tasks of numerous
artificial intelligence (AI)-related applications. However, their ability in
handling complex practical problems remains limited, particularly for HS data,
due to the effects of various spectral variabilities in the process of HS
imaging and the complexity and redundancy of higher dimensional HS signals.
Compared to the convex models, non-convex modeling, which is capable of
characterizing more complex real scenes and providing the model
interpretability technically and theoretically, has been proven to be a
feasible solution to reduce the gap between challenging HS vision tasks and
currently advanced intelligent data processing models
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