Manifold alignment using Procrustes analysis

By Chang Wang, Sridhar Mahadevan. International Conference on Machine Learning, 2008

308 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Control and Systems Engineering, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/a6c3392d12b00a4fac8b82dd9a1ff7bad8f29b74

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

In Control and Systems Engineering

In Mechanical Engineering

Citations in the corpus

Data-Driven Intelligent Transportation Systems: A Survey (2011, background): “For example, both canonical correlation [116] and Procrustes analyses [117] are devoted to aligning two heterogeneous data into a common space.”
Bilingual Word Embeddings from Parallel and Non-parallel Corpora for Cross-Language Text Classification (2016, methodology): “• In the first step, we perform manifold alignment using Procrustes analysis (Wang and Mahadevan, 2008) between sets of documents belonging to same class/label in different languages.”
A Survey on Transfer Learning (2010, methodology): “In [44], Wang and Mahadevan proposed a Procrustes analysis-based approach to manifold alignment without correspondences, which can be used to transfer the knowledge across domains via the aligned manifolds.”
Machine learning and feature selection for drug response prediction in precision oncology applications (2018, methodology): “…blurring mean shift (GBMS; Wang and Carreira-Perpinán 2010), (ii) optimal alignment between training and auxiliary data using procrustes analyses (Wang and Mahadevan 2008), and (iii) applying standard machine learning method (e.g., support vector regression with linear or sigmoid kernel ridge…”
Parallel field alignment for cross media retrieval (2013, background): “And in the same period, manifold alignment methods [10, 29, 31] are proposed and shown that they are appropriate joint models for pair matching between heterogeneous data sources.”
A Comprehensive Survey of Privacy-preserving Federated Learning (2021, background): “• Feature-based FTL aims to minimize domain divergence and learn a “good” feature representation for the target domain, and thus it can effectively encode the transformation knowledge from the source domain to the target domain [90, 183].”
Multimodal Manifold Analysis by Simultaneous Diagonalization of Laplacians (2015, unknown): “In Section 4, we show that many recent works on multi-view clustering [25], [26], [27], [28], [29] and manifold alignment [30], [31] can be considered as particular instances of our framework.”
Feature-based transfer learning with real-world applications (2010, methodology): “In [193], Wang and Mahadevan proposed a Procrustes analysis based approach to manifold alignment without correspondences, which can be used to transfer the knowledge across domains via the aligned manifolds.”
Boosting for transfer learning with multiple sources (2010, methodology): “The approaches to transfer learning are categorized based on the means used for importing knowledge from source to target, and can be based on instance-transfer [4, 1], feature-representation-transfer [17, 24], parameter-transfer [8, 2, 19], and relationalknowledge-transfer.”
Transfer across Completely Different Feature Spaces via Spectral Embedding (2013, background): “One line of research is to find a new feature space in which the training and test data have strong similarities (e.g., [9], [11], [14], [13], [24]).”
A Survey of Multi-View Representation Learning (2016, unknown): “Further, Wang and Mahadevan [292] also introduce a twostage approach for manifold alignment using procrustes analysis.”