Fast and Robust Archetypal Analysis for Representation Learning

Chen, Yuansi; Mairal, Julien; Harchaoui, Zaïd

doi:10.1109/cvpr.2014.192

Cited by 72 publications

(79 citation statements)

References 26 publications

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“…Note that the L-PET based estimation and the MRI based estimation can both be obtained with different number of levels for common space learning. The optimal values for ω 1 and ω 2 can be efficiently computed using a recently proposed active-set algorithm [32]. …”

Section: Methodsmentioning

confidence: 99%

Multi-Level Canonical Correlation Analysis for Standard-Dose PET Image Estimation

Zhang

Adeli

et al. 2016

IEEE Trans. on Image Process.

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Positron emission tomography (PET) images are widely used in many clinical applications such as tumor detection and brain disorder diagnosis. To obtain PET images of diagnostic quality, a sufficient amount of radioactive tracer has to be injected into a living body, which will inevitably increase the risk of radiation exposure. On the other hand, if the tracer dose is considerably reduced, the quality of the resulting images would be significantly degraded. It is of great interest to estimate a standard-dose PET (S-PET) image from a low-dose one in order to reduce the risk of radiation exposure and preserve image quality. This may be achieved through mapping both standard-dose and low-dose PET data into a common space and then performing patch based sparse representation. However, a one-size-fits-all common space built from all training patches is unlikely to be optimal for each target S-PET patch, which limits the estimation accuracy. In this paper, we propose a data-driven multi-level Canonical Correlation Analysis (mCCA) scheme to solve this problem. Specifically, a subset of training data that is most useful in estimating a target S-PET patch is identified in each level, and then used in the next level to update common space and improve estimation. Additionally, we also use multi-modal magnetic resonance images to help improve the estimation with complementary information. Validations on phantom and real human brain datasets show that our method effectively estimates S-PET images and well preserves critical clinical quantification measures, such as standard uptake value.

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Section: Methodsmentioning

confidence: 99%

Multi-Level Canonical Correlation Analysis for Standard-Dose PET Image Estimation

Zhang

Adeli

et al. 2016

IEEE Trans. on Image Process.

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“…The simplex constraints inherently enforces sparseness i.e., only a few of the archetypes in D will contribute to a t [8].…”

Section: Proposed Sparse Convex Sequence Kernel For Badmentioning

confidence: 99%

“…In order to select a suitable subset, we used the Fast Exemplar Selection (FES) algorithm as proposed in [20], which extracts a linearly independent subset of signals which captures the full range of the training dataset. AA is performed using fast implementations provided by SPAMS toolbox [8], where the tolerance of 10 −3 is used as the stopping criteria. For SVM training, Libsvm toolbox [24] is used.…”

Section: B Experimental Setupmentioning

confidence: 99%

“…The entries in the weight vector a i represent the contribution dictionary atoms in the signal. Due to the inherent sparsity property of convex representations [8], the obtained weight vector for bird and non-bird class signals is significantly different. Since archetypes are learned from only bird class, non-bird class signals will have a denser representation compared to the bird counterpart, which helps in effective discrimination.…”

Section: Introductionmentioning

confidence: 99%

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Archetypal analysis based sparse convex sequence kernel for bird activity detection

Abrol

Sharma

Thakur

et al. 2017

2017 25th European Signal Processing Conference (EUSIPCO)

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Abstract-This paper proposes a novel method based on the archetypal analysis (AA) for bird activity detection (BAD) task. The proposed method extracts a convex representation (frame-wise) by projecting a given audio signal on to a learned dictionary. The AA based dictionary is trained only on bird class signals, which makes the method robust to background noise. Further, it is shown that due to the inherent sparsity property of convex representations, non-bird class signals will have a denser representation as compared to the bird counterpart, which helps in effective discrimination. In order to detect presence/absence of bird vocalization, a fixed length representation is obtained by averaging the obtained frame wise representations of an audio signal. Classification of these fixed length representations is performed using support vector machines (SVM) with a dynamic kernel. In this work, we propose a variant of probabilistic sequence kernel called sparse convex sequence kernel (SCSK) for the BAD task. Experimental results show that the proposed method can efficiently discriminate bird from non-bird class signals.

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“…Archetypal analysis assumes that archetypes are convex combinations of all band points and all band points are approximated in terms of convex combinations of archetypes [49]. Therefore, selecting a proper band subset is then explained as finding archetypes of the minimal convex hull of the high-dimensional band points [50]. The coefficient matrices B and A in Equation (5) are unknown, and that makes the un-convex optimization problem challenging to solve.…”

Section: The Solution Of Ssr Model For Band Selectionmentioning

confidence: 99%

A Symmetric Sparse Representation Based Band Selection Method for Hyperspectral Imagery Classification

Sun

Jiang

et al. 2016

Remote Sensing

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Abstract:A novel Symmetric Sparse Representation (SSR) method has been presented to solve the band selection problem in hyperspectral imagery (HSI) classification. The method assumes that the selected bands and the original HSI bands are sparsely represented by each other, i.e., symmetrically represented. The method formulates band selection into a famous problem of archetypal analysis and selects the representative bands by finding the archetypes in the minimal convex hull containing the HSI band points (i.e., one band corresponds to a band point in the high-dimensional feature space). Without any other parameter tuning work except the size of band subset, the SSR optimizes the band selection program using the block-coordinate descent scheme. Four state-of-the-art methods are utilized to make comparisons with the SSR on the Indian Pines and PaviaU HSI datasets. Experimental results illustrate that SSR outperforms all four methods in classification accuracies (i.e., Average Classification Accuracy (ACA) and Overall Classification Accuracy (OCA)) and three quantitative evaluation results (i.e., Average Information Entropy (AIE), Average Correlation Coefficient (ACC) and Average Relative Entropy (ARE)), whereas it takes the second shortest computational time. Therefore, the proposed SSR is a good alternative method for band selection of HSI classification in realistic applications.

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Fast and Robust Archetypal Analysis for Representation Learning

Cited by 72 publications

References 26 publications

Multi-Level Canonical Correlation Analysis for Standard-Dose PET Image Estimation

Multi-Level Canonical Correlation Analysis for Standard-Dose PET Image Estimation

Archetypal analysis based sparse convex sequence kernel for bird activity detection

A Symmetric Sparse Representation Based Band Selection Method for Hyperspectral Imagery Classification

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