Tensor-Dictionary Learning with Deep Kruskal-Factor Analysis

Andrew Stevens, Yunchen Pu, Yannan Sun, Gregory Spell, Lawrence Carin
; Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, PMLR 54:121-129, 2017.

Abstract

A multi-way factor analysis model is introduced for tensor-variate data of any order. Each data item is represented as a (sparse) sum of Kruskal decompositions, a Kruskal- factor analysis (KFA). KFA is nonparametric and can infer both the tensor-rank of each dictionary atom and the number of dictionary atoms. The model is adapted for online learning, which allows dictionary learning on large data sets. After KFA is introduced, the model is extended to a deep convolutional tensor-factor analysis, supervised by a Bayesian SVM. The experiments section demonstrates the improvement of KFA over vectorized approaches (e.g., BPFA), tensor decompositions, and convolutional neural networks (CNN) in multi-way denoising, blind inpainting, and image classification. The improvement in PSNR for the inpainting results over other methods exceeds 1dB in several cases and we achieve state of the art results on Caltech101 image classification.

Cite this Paper


BibTeX
@InProceedings{pmlr-v54-stevens17a, title = {{Tensor-Dictionary Learning with Deep Kruskal-Factor Analysis}}, author = {Andrew Stevens and Yunchen Pu and Yannan Sun and Gregory Spell and Lawrence Carin}, booktitle = {Proceedings of the 20th International Conference on Artificial Intelligence and Statistics}, pages = {121--129}, year = {2017}, editor = {Aarti Singh and Jerry Zhu}, volume = {54}, series = {Proceedings of Machine Learning Research}, address = {Fort Lauderdale, FL, USA}, month = {20--22 Apr}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v54/stevens17a/stevens17a.pdf}, url = {http://proceedings.mlr.press/v54/stevens17a.html}, abstract = {A multi-way factor analysis model is introduced for tensor-variate data of any order. Each data item is represented as a (sparse) sum of Kruskal decompositions, a Kruskal- factor analysis (KFA). KFA is nonparametric and can infer both the tensor-rank of each dictionary atom and the number of dictionary atoms. The model is adapted for online learning, which allows dictionary learning on large data sets. After KFA is introduced, the model is extended to a deep convolutional tensor-factor analysis, supervised by a Bayesian SVM. The experiments section demonstrates the improvement of KFA over vectorized approaches (e.g., BPFA), tensor decompositions, and convolutional neural networks (CNN) in multi-way denoising, blind inpainting, and image classification. The improvement in PSNR for the inpainting results over other methods exceeds 1dB in several cases and we achieve state of the art results on Caltech101 image classification. } }
Endnote
%0 Conference Paper %T Tensor-Dictionary Learning with Deep Kruskal-Factor Analysis %A Andrew Stevens %A Yunchen Pu %A Yannan Sun %A Gregory Spell %A Lawrence Carin %B Proceedings of the 20th International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2017 %E Aarti Singh %E Jerry Zhu %F pmlr-v54-stevens17a %I PMLR %J Proceedings of Machine Learning Research %P 121--129 %U http://proceedings.mlr.press %V 54 %W PMLR %X A multi-way factor analysis model is introduced for tensor-variate data of any order. Each data item is represented as a (sparse) sum of Kruskal decompositions, a Kruskal- factor analysis (KFA). KFA is nonparametric and can infer both the tensor-rank of each dictionary atom and the number of dictionary atoms. The model is adapted for online learning, which allows dictionary learning on large data sets. After KFA is introduced, the model is extended to a deep convolutional tensor-factor analysis, supervised by a Bayesian SVM. The experiments section demonstrates the improvement of KFA over vectorized approaches (e.g., BPFA), tensor decompositions, and convolutional neural networks (CNN) in multi-way denoising, blind inpainting, and image classification. The improvement in PSNR for the inpainting results over other methods exceeds 1dB in several cases and we achieve state of the art results on Caltech101 image classification.
APA
Stevens, A., Pu, Y., Sun, Y., Spell, G. & Carin, L.. (2017). Tensor-Dictionary Learning with Deep Kruskal-Factor Analysis. Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, in PMLR 54:121-129

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