Model selection and explainability in neural networks using a polytope interpolation framework

Shekkizhar, Sarath; Ortega, Antonio

doi:10.1109/ieeeconf53345.2021.9723382

Cited by 7 publications

(8 citation statements)

References 12 publications

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“…NNK has been shown to perform well in several machine learning tasks [15], image representation [16], and generalization estimation in neural networks [17]. Furthermore, NNK has also been used to understand convolutional neural networks (CNN) channel redundancy [18] and to propose an early stopping criterion for them [19].…”

Section: Non-negative Kernel (Nnk) Regression Graphsmentioning

confidence: 99%

Study of Manifold Geometry Using Multiscale Non-Negative Kernel Graphs

Hurtado

Shekkizhar

Ruiz-Hidalgo

et al. 2023

ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Modern machine learning systems are increasingly trained on large amounts of data embedded in high-dimensional spaces. Often this is done without analyzing the structure of the dataset. In this work, we propose a framework to study the geometric structure of the data. We make use of our recently introduced non-negative kernel (NNK) regression graphs to estimate the point density, intrinsic dimension, and linearity of the data manifold (curvature). We further generalize the graph construction and geometric estimation to multiple scales by iteratively merging neighborhoods in the input data. Our experiments demonstrate the effectiveness of our proposed approach over other baselines in estimating the local geometry of the data manifolds on synthetic and real datasets.

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Section: Non-negative Kernel (Nnk) Regression Graphsmentioning

confidence: 99%

Study of Manifold Geometry Using Multiscale Non-Negative Kernel Graphs

Hurtado

Shekkizhar

Ruiz-Hidalgo

et al. 2023

ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Self Cite

View full text Add to dashboard Cite

show abstract

“…NNK has been shown to deliver good results in several machine learning tasks [15], image representation [16], and generalization estimation in neural networks [17]. Furthermore, NNK has also been used to understand convolutional neural networks (CNN) channel redundancy [18] and to propose an early stopping criterion for them [19].…”

Section: Non-negative Kernel (Nnk) Regression Graphsmentioning

confidence: 99%

Study of Manifold Geometry using Multiscale Non-Negative Kernel Graphs

Hurtado¹,

Shekkizhar²,

Ruiz-Hidalgo³

et al. 2022

Preprint

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Modern machine learning systems are increasingly trained on large amounts of data embedded in high-dimensional spaces. Often this is done without analyzing the structure of the dataset. In this work, we propose a framework to study the geometric structure of the data. We make use of our recently introduced non-negative kernel (NNK) regression graphs to estimate the point density, intrinsic dimension, and the linearity of the data manifold (curvature). We further generalize the graph construction and geometric estimation to multiple scale by iteratively merging neighborhoods in the input data. Our experiments demonstrate the effectiveness of our proposed approach over other baselines in estimating the local geometry of the data manifolds on synthetic and real datasets.

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“…For these reasons, we make use of non-negative kernel regression (NNK) [31] to define neighborhoods and graphs for our manifold analysis. Unlike KNN, which is a thresholding approximation, NNK can be viewed as a form of basis pursuit [41] and results in better neighborhood construction with improved and robust local estimation performance in various machine learning tasks [13], [32]. The key advantage of NNK is that it has a geometric interpretation for each neighborhood constructed.…”

Section: Supplementary Materials a Non-negative Kernel Regression (Nn...mentioning

confidence: 99%

“…This allows a distance to be computed between actions with different durations. We choose the NNK construction due to its robust performance in local estimation across different machine learning tasks [13]. The benefits of the NNK construction will be demonstrated through a comparison with k-NN DS-Graph constructions in Section IV.B.…”

Section: Introductionmentioning

confidence: 99%

Symmetric Sub-graph Spatio-Temporal Graph Convolution and its application in Complex Activity Recognition

Das

Ortega

2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Understanding complex hand actions, such as assembly tasks or kitchen activities, from hand skeleton data is an important yet challenging task. In this paper, we analyze hand skeleton-based complex activities by modeling dynamic hand skeletons through a spatiotemporal graph convolutional neural network (ST-GCN). This model jointly learns and extracts Spatio-temporal features for activity recognition. Our proposed technique, Symmetric Sub-graph spatio-temporal graph convolutional neural network (S 2 -ST-GCN), exploits the symmetric nature of hand graphs to decompose them into smaller sub-graphs, which allow us to build a separate temporal model for the relative motion of the fingers. This subgraph approach can be implemented efficiently by preprocessing input data using a Haar unit based orthogonal matrix. Then, in addition to spatial filters, separate temporal filters can be learned for each sub-graph. We evaluate the performance of the proposed method on the First-Person Hand Action dataset. While the proposed method shows comparable performance with the state of the art methods in train:test=1:1 setting, it achieves this with greater stability. Furthermore, we demonstrate significant performance improvement in comparison to state of the art methods in the cross-person setting, where the model did not come across a test subject's data while learning. S 2 -ST-GCN also shows superior performance than a finger-based decomposition of the hand graph where no preprocessing is applied.Index Terms-Hand Skeleton, Graph based methods, complex activity analysis, Spatio-temporal graph neural network, First person hand action (FPHA) dataset

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Model selection and explainability in neural networks using a polytope interpolation framework

Cited by 7 publications

References 12 publications

Study of Manifold Geometry Using Multiscale Non-Negative Kernel Graphs

Study of Manifold Geometry Using Multiscale Non-Negative Kernel Graphs

Study of Manifold Geometry using Multiscale Non-Negative Kernel Graphs

Symmetric Sub-graph Spatio-Temporal Graph Convolution and its application in Complex Activity Recognition

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