Fast adaptive eigenvalue decomposition: a maximum likelihood approach

Chonavel, Thierry; Champagne, Benoı̂t; Riou, Christian

doi:10.1016/s0165-1684(02)00417-6

Cited by 22 publications

(15 citation statements)

References 23 publications

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“…The singular value decomposition (SVD) step in SVF is by far the most intensive computation in either technique with each SVD representing N 3 operations (Yu and Lovstakken, 2010). Even without optimization, which has been shown to achieve dramatically reduced SVD computational cost (Lovstakken et al , 2006; Chonavel et al , 2003; Mauldin et al , 2010), the SVF technique can be implemented, using dedicated hardware ( e.g. GPUs), to produce frame rates greater than 30 Hz.…”

Section: Discussionmentioning

confidence: 99%

Real-time targeted molecular imaging using singular value spectra properties to isolate the adherent microbubble signal

et al. 2012

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Ultrasound-based real-time molecular imaging in large blood vessels holds promise for early detection and diagnosis of various important and significant diseases, such as stroke, atherosclerosis, and cancer. Central to the success of this imaging technique is the isolation of ligand-receptor bound adherent microbubbles from free microbubbles and tissue structures. In this paper, we present an alternative approach, termed singular spectrum-based targeted molecular (SiSTM) imaging, which separates signal components using singular value spectra content over local regions of complex echo data. Simulations were performed to illustrate the effects of acoustic target motion and harmonic energy on SiSTM imaging-derived measurements of statistical dimensionality. In vitro flow phantom experiments were performed under physiologically realistic conditions (2.7 cm/s flow velocity and 4 mm diameter) with targeted and non-targeted phantom channels. Both simulation and experimental results demonstrated that the relative motion and harmonic characteristics of adherent microbubbles (i.e. low motion and large harmonics) yields echo data with dimensionality that is distinct from free microbubbles (i.e. large motion and large harmonics) and tissue (i.e. low motion and low harmonics). Experimental SiSTM images produced the expected trend of greater adherent microbubble signal in targeted versus non-targeted microbubble experiments (P < 0.05, n = 4). The location of adherent microbubbles was qualitatively confirmed via optical imaging of fluorescent DiI signal along the phantom channel walls after SiSTM imaging. In comparison with two frequency-based real-time molecular imaging strategies, SiSTM imaging provided significantly higher image contrast (P < 0.001, n = 4) and larger area under the receiver operating characteristic curve (P < 0.05, n = 4).

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Section: Discussionmentioning

confidence: 99%

Real-time targeted molecular imaging using singular value spectra properties to isolate the adherent microbubble signal

et al. 2012

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“…By applying eigenvalue decomposition (EVD) [14] with B, we can easily obtain the (n + k)-dimensional embedded representation O that models the (n+k)-dimensional embedded manifold (R n+k ).…”

Section: Embedded Manifold Extractionmentioning

confidence: 99%

AMS-SFE: Towards an Alignment of Manifold Structures via Semantic Feature Expansion for Zero-shot Learning

Guo

2019

2019 IEEE International Conference on Multimedia and Expo (ICME)

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Zero-shot learning (ZSL) aims at recognizing unseen classes with knowledge transferred from seen classes. This is typically achieved by exploiting a semantic feature space (FS) shared by both seen and unseen classes, i.e., attributes or word vectors, as the bridge. However, due to the mutually disjoint of training (seen) and testing (unseen) data, existing ZSL methods easily and commonly suffer from the domain shift problem. To address this issue, we propose a novel model called AMS-SFE. It considers the Alignment of Manifold Structures by Semantic Feature Expansion. Specifically, we build up an autoencoder based model to expand the semantic features and joint with an alignment to an embedded manifold extracted from the visual FS of data. It is the first attempt to align these two FSs by way of expanding semantic features. Extensive experiments show the remarkable performance improvement of our model compared with other existing methods.

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“…The overall computational cost of FAPI is n(3r + 2) + 5r 2 + O(r) flops per iteration 13 (whereas the complexities of PAST [22] and OPAST [25] are respectively 3nr + 2r 2 + O(r) and n(4r + 1) + 2r 2 + O(r)). The overall computational cost of TW-FAPI is n(6r + 8) + 4lr + O(r 2 ) flops per iteration 14 (whereas the complexities of SW-PAST and SW-OPAST [29] are respectively 5nr + 4r 2 + O(r) and n(15r + 28) + 12r 2 + O(r)).…”

Section: B Fast Implementation Of the Particular Solutionmentioning

confidence: 99%

“…Other techniques rely on the noise and signal subspace averaging method [12], the maximum likelihood principle [13], the operator restriction analysis [14], or the perturbation theory [15].…”

Section: Introductionmentioning

confidence: 99%

Fast approximated power iteration subspace tracking

Badeau¹,

David²,

Richard³

2005

IEEE Trans. Signal Process.

167

125

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Abstract-This paper introduces a fast implementation of the power iteration method for subspace tracking, based on an approximation less restrictive than the well known projection approximation. This algorithm, referred to as the fast API method, guarantees the orthonormality of the subspace weighting matrix at each iteration. Moreover, it outperforms many subspace trackers related to the power iteration method, such as PAST, NIC, NP3 and OPAST, while having the same computational complexity. The API method is designed for both exponential windows and sliding windows. Our numerical simulations show that sliding windows offer a faster tracking response to abrupt signal variations.

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Fast adaptive eigenvalue decomposition: a maximum likelihood approach

Cited by 22 publications

References 23 publications

Real-time targeted molecular imaging using singular value spectra properties to isolate the adherent microbubble signal

Real-time targeted molecular imaging using singular value spectra properties to isolate the adherent microbubble signal

AMS-SFE: Towards an Alignment of Manifold Structures via Semantic Feature Expansion for Zero-shot Learning

Fast approximated power iteration subspace tracking

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