Localized Multiple Kernel Learning With Dynamical Clustering and Matrix Regularization

Han, Yina; Yang, Kunde; Yang, Yixin; Ma, Yuanliang

doi:10.1109/tnnls.2016.2635151

Cited by 24 publications

(7 citation statements)

References 26 publications

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“…In contrary, the more commonly used vector p -norm regularisation [29] lacks an explicit mechanism to capture inter-kernel interactions. As such, the matrix-norm regularisation has been observed to outperform its vector-norm counterpart in different settings [8], [37], [34], [38]. Note that the vector-norm constraint may be considered as a special case of the matrix-norm constraint as setting p = q would reduce the matrix-norm into a vector-norm.…”

Section: Preliminariesmentioning

confidence: 99%

“…First, the MKL problem is formulated in the presence of an p -norm constraint on kernel weights. More recent studies have demonstrated that a more general matrix p,q -norm regularisation on kernel weights may yield better performance in different applications settings [8], [37], [34], [38]. Second, the localised MKL method in [36] decouples the learning tasks into a set of disjoint MKL problems each associated with a distinct region in the data.…”

Section: Introductionmentioning

confidence: 99%

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Unknown Face Presentation Attack Detection via Localised Learning of Multiple Kernels

Arashloo¹

2022

Preprint

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The paper studies face spoofing, a.k.a. presentation attack detection (PAD) in the demanding scenarios of unknown types of attack. While earlier studies have revealed the benefits of ensemble methods, and in particular, a multiple kernel learning approach to the problem, one limitation of such techniques is that they typically treat the entire observation space similarly and ignore any variability and local structure inherent to the data. This work studies this aspect of the face presentation attack detection problem in relation to multiple kernel learning in a oneclass setting to benefit from intrinsic local structure in bona fide face samples. More concretely, inspired by the success of the one-class Fisher null formalism, we formulate a convex localised multiple kernel learning algorithm by imposing a joint matrixnorm constraint on the collection of local kernel weights and infer locally adaptive weights for zero-shot one-class unseen attack detection.We present a theoretical study of the proposed localised MKL algorithm using Rademacher complexities to characterise its generalisation capability and demonstrate the advantages of the proposed technique over some other options. An assessment of the proposed approach on general object image datasets illustrates its efficacy for abnormality and novelty detection while the results of the experiments on face PAD datasets verifies its potential in detecting unknown/unseen face presentation attacks.

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Section: Preliminariesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Unknown Face Presentation Attack Detection via Localised Learning of Multiple Kernels

Arashloo¹

2022

Preprint

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“…In the context of multiple kernel learning (MKL), while the vast majority of existing algorithms are global in nature, meaning the kernel weights are shared across the whole observation space, the issue was initially identified in [30] and has received considerable attention since then [31], [32], [33], [34], [35]. However, existing localised multiple kernel learning methods use non-convex objective functions which raises questions with regards to their generalisation capacities.…”

Section: Introductionmentioning

confidence: 99%

Unknown Face Presentation Attack Detection via Localized Learning of Multiple Kernels

Arashloo

2023

IEEE Trans.Inform.Forensic Secur.

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“…Multiple kernel clustering (MKC) aims to extract complementary information from multiple pre-specified kernels and then categorize the data with close patterns or structures into the same cluster (Zhao, Kwok, and Zhang 2009;Kloft, Rückert, and Bartlett 2010;Kloft et al 2011;Yu et al 2011;Huang, Chuang, and Chen 2011;Gönen and Alpaydın 2011;Zhou et al 2015;Han et al 2016;Wang et al 2017b;Zhou et al 2021a;Liu et al 2021a). Due to the ability to mining inherent non-linear information, MKC has been intensively researched and commonly applied to various applications (Liu et al 2016;Liu et al 2017;Bhadra, Kaski, and Rousu 2017;Liu et al 2019;Zhou et al 2019Zhou et al , 2021b.…”

Section: Introductionmentioning

confidence: 99%

Fusion Multiple Kernel K-means

Zhang

Liu

et al. 2022

AAAI

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Multiple kernel clustering aims to seek an appropriate combination of base kernels to mine inherent non-linear information for optimal clustering. Late fusion algorithms generate base partitions independently and integrate them in the following clustering procedure, improving the overall efficiency. However, the separate base partition generation leads to inadequate negotiation with the clustering procedure and a great loss of beneficial information in corresponding kernel matrices, which negatively affects the clustering performance. To address this issue, we propose a novel algorithm, termed as Fusion Multiple Kernel k-means (FMKKM), which unifies base partition learning and late fusion clustering into one single objective function, and adopts early fusion technique to capture more sufficient information in kernel matrices. Specifically, the early fusion helps base partitions keep more beneficial kernel details, and the base partitions learning further guides the generation of consensus partition in the late fusion stage, while the late fusion provides positive feedback on two former procedures. The close collaboration of three procedures results in a promising performance improvement. Subsequently, an alternate optimization method with promising convergence is developed to solve the resultant optimization problem. Comprehensive experimental results demonstrate that our proposed algorithm achieves state-of-the-art performance on multiple public datasets, validating its effectiveness. The code of this work is publicly available at https://github.com/ethan-yizhang/Fusion-Multiple-Kernel-K-means.

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Localized Multiple Kernel Learning With Dynamical Clustering and Matrix Regularization

Cited by 24 publications

References 26 publications

Unknown Face Presentation Attack Detection via Localised Learning of Multiple Kernels

Unknown Face Presentation Attack Detection via Localised Learning of Multiple Kernels

Unknown Face Presentation Attack Detection via Localized Learning of Multiple Kernels

Fusion Multiple Kernel K-means

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