Proceedings Article10.1109/ICCV.2007.4408855
Spectral Regression for Efficient Regularized Subspace Learning
Deng Cai,Xiaofei He,Jiawei Han +2 more
- 26 Dec 2007
- pp 1-8
TL;DR: This paper proposes a novel dimensionality reduction framework, called spectral regression (SR), for efficient regularized subspace learning, which casts the problem of learning the projective functions into a regression framework, which avoids eigen-decomposition of dense matrices.
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Abstract: Subspace learning based face recognition methods have attracted considerable interests in recent years, including principal component analysis (PCA), linear discriminant analysis (LDA), locality preserving projection (LPP), neighborhood preserving embedding (NPE) and marginal Fisher analysis (MFA). However, a disadvantage of all these approaches is that their computations involve eigen- decomposition of dense matrices which is expensive in both time and memory. In this paper, we propose a novel dimensionality reduction framework, called spectral regression (SR), for efficient regularized subspace learning. SR casts the problem of learning the projective functions into a regression framework, which avoids eigen-decomposition of dense matrices. Also, with the regression based framework, different kinds of regularizes can be naturally incorporated into our algorithm which makes it more flexible. Computational analysis shows that SR has only linear-time complexity which is a huge speed up comparing to the cubic-time complexity of the ordinary approaches. Experimental results on face recognition demonstrate the effectiveness and efficiency of our method.
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Citations
Joint Embedding Learning and Sparse Regression: A Framework for Unsupervised Feature Selection
TL;DR: This paper proposes a novel unsupervised feature selection framework, termed as the joint embedding learning and sparse regression (JELSR), in which the embedding learned with sparse regression to perform feature selection.
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Joint Feature Selection and Subspace Learning for Cross-Modal Retrieval
TL;DR: An iterative algorithm is presented to solve the proposed joint learning problem, along with its convergence analysis, and Experimental results on cross-modal retrieval tasks demonstrate that the proposed method outperforms the state-of-the-art subspace approaches.
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Discriminative multi-manifold analysis for face recognition from a single training sample per person
Jiwen Lu,Yap-Peng Tan,Gang Wang +2 more
- 06 Nov 2011
TL;DR: A novel discriminative multi-manifold analysis (DMMA) method by learning discriminating features from image patches is proposed to address the problem of not enough samples for discriminant learning in appearance-based face recognition methods.
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Non-invasive Monitoring of Intracranial Pressure Using Transcranial Doppler Ultrasonography: Is It Possible?
Danilo Cardim,Chiara Robba,Michal Bohdanowicz,Joseph Donnelly,Brenno Caetano Troca Cabella,Xiuyun Liu,Manuel Cabeleira,Peter Smielewski,Bernhard Schmidt,Marek Czosnyka +9 more
TL;DR: Overall accuracy for TCD-based methods ranges around ±12 mmHg, with a great potential of tracing dynamical changes of ICP in time, particularly those of vasogenic nature.
Coupled Spectral Regression for matching heterogeneous faces
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- 20 Jun 2009
TL;DR: This paper presents a subspace learning framework named Coupled Spectral Regression (CSR) to solve the challenge problem of coupling the two types of face images and matching between them, and shows that the proposed CSR method significantly outperforms the existing methods.
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