Multiset canonical correlations analysis and multispectral, truly multitemporal remote sensing data

By Allan Aasbjerg Nielsen. IEEE Transactions on Image Processing, 2002

312 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Ecology, Media Technology

View Paper: https://www.semanticscholar.org/paper/cc1e3f9a74373ec6d0deb9ce83717c1252d8ac87

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Citations in the corpus

A Novel Framework for the Design of Change-Detection Systems for Very-High-Resolution Remote Sensing Images (2013, methodology): “…are defined by using: i) fuzzy set theory [51], [52], [53], [54], [55], [56], [57], [58], [59]; ii) data-driven transformations (e.g., Principal Component Analysis [37]; Independent Component Analysis [60], Multivariate Alteration Detection [36], [61], [62]); and iii) similarity measures [63].”
Multi-sensor data fusion for estimating forest species composition and abundance in northern Minnesota (2011, methodology): “Nielsen (2002) described the use of canonical correlation analysis (CCA) and partial least squares (PLS) regression as effective means of achieving data fusion, since both are used to transform the original data into new orthogonal variables or latent components.”
Discriminant Correlation Analysis: Real-Time Feature Level Fusion for Multimodal Biometric Recognition (2016, methodology): “Although Kettenrings and Nielsens methods [15], [16] are able to analyze multi-”
The Regularized Iteratively Reweighted MAD Method for Change Detection in Multi- and Hyperspectral Data (2007, unknown): “Multiset canonical correlation analysis where we investigate the relationship between more than two groups of several variables first introduced in [24], [25] is described and applied to remote sensing data in [6] and [26].”
Scalable Deep Multimodal Learning for Cross-Modal Retrieval (2019, methodology): “One typical kind of methods attempts to project the multimodal data into a latent common subspace by maximizing the correlation between different modalities, such as Canonical Correlation Analysis (CCA) [7], Partial Least Squares (PLS) [34], and Multi-set CCA (MCCA) [22, 33].”
Integrating single-cell transcriptomic data across different conditions, technologies, and species (2018, methodology): “We use CCA for pairwise integration of two data sets, and extend this to multi-set CCA (multi-CCA) 33,34 for the integration of multiple data sets.”
Multimodal Data Fusion: An Overview of Methods, Challenges, and Prospects (2015, methodology): “1); covariations, as in CCA [1] and its extension to more than two matrices, multiset CCA (MCCA) [3], [58], [110], [118],”
Frequency Recognition in SSVEP-Based BCI using Multiset Canonical Correlation Analysis (2013, methodology): “The ﬁve most discussed objec-tive functions 29 , 35 for MsetCCA are: (1) SUMCOR, maximize the sum of all entries in the correlation matrix; (2) SSQCOR, maximize the sum of squares of all entries in the correlation matrix; (3) MAX-VAR, maximize the largest eigenvalue of the correlation matrix; (4)…”