University of Hertfordshire

By the same authors

Sparse feature extraction model with Independent Subspace Analysis

Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)peer-review

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Sparse feature extraction model with Independent Subspace Analysis. / Nath, Radhika; Manjunathaiah, Manju.

Machine Learning, Optimization, and Data Science. ed. / G Nicosia ; P Pardalos; G Giuffrida; R Umeton; V Sciacca. Springer, 2019. p. 494-505 ( Lecture Notes in Computer Science ; Vol. 11331).

Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)peer-review

Harvard

Nath, R & Manjunathaiah, M 2019, Sparse feature extraction model with Independent Subspace Analysis. in G Nicosia , P Pardalos, G Giuffrida, R Umeton & V Sciacca (eds), Machine Learning, Optimization, and Data Science. Lecture Notes in Computer Science , vol. 11331, Springer, pp. 494-505, 4th International Conference, Volterra, Italy, 13/09/18. https://doi.org/10.1007/978-3-030-13709-0_42

APA

Nath, R., & Manjunathaiah, M. (2019). Sparse feature extraction model with Independent Subspace Analysis. In G. Nicosia , P. Pardalos, G. Giuffrida, R. Umeton, & V. Sciacca (Eds.), Machine Learning, Optimization, and Data Science (pp. 494-505). ( Lecture Notes in Computer Science ; Vol. 11331). Springer. https://doi.org/10.1007/978-3-030-13709-0_42

Vancouver

Nath R, Manjunathaiah M. Sparse feature extraction model with Independent Subspace Analysis. In Nicosia G, Pardalos P, Giuffrida G, Umeton R, Sciacca V, editors, Machine Learning, Optimization, and Data Science. Springer. 2019. p. 494-505. ( Lecture Notes in Computer Science ). https://doi.org/10.1007/978-3-030-13709-0_42

Author

Nath, Radhika ; Manjunathaiah, Manju. / Sparse feature extraction model with Independent Subspace Analysis. Machine Learning, Optimization, and Data Science. editor / G Nicosia ; P Pardalos ; G Giuffrida ; R Umeton ; V Sciacca. Springer, 2019. pp. 494-505 ( Lecture Notes in Computer Science ).

Bibtex

@inbook{93224c64ffaa49d7b0d8d5265965e9fc,
title = "Sparse feature extraction model with Independent Subspace Analysis",
abstract = "Recent advances in deep learning models have demonstrated remarkable accuracy in object classification. However, the limitations of Convolutional Neural Networks such as the requirement for a large collection of labeled data for training and supervised learning process has called for enhanced feature representation and for unsupervised models.In this paper we propose a novel unsupervised sparsity-based model using Independent Subspace Analysis (ISA) to implement a hierarchical network for feature extraction. The results of our empirical evaluation demonstrates an improved classification accuracy when max pooling is paired with square pooling within each layer. In addition to accuracy, we further show that it also reduces the data dimensions within the layers outperforming known sparsity-based models.",
keywords = "Biologically inspired vision models, Convolutional neural networks, Deep learning, Sparse models",
author = "Radhika Nath and Manju Manjunathaiah",
note = "{\textcopyright} Springer Nature Switzerland AG 2019; 4th International Conference : LOD 2018 ; Conference date: 13-09-2018 Through 16-09-2018",
year = "2019",
month = feb,
day = "14",
doi = "10.1007/978-3-030-13709-0_42",
language = "English",
isbn = "978-3-030-13708-3",
series = " Lecture Notes in Computer Science ",
publisher = "Springer",
pages = "494--505",
editor = "{Nicosia }, G and P Pardalos and G Giuffrida and R Umeton and V Sciacca",
booktitle = "Machine Learning, Optimization, and Data Science",

}

RIS

TY - CHAP

T1 - Sparse feature extraction model with Independent Subspace Analysis

AU - Nath, Radhika

AU - Manjunathaiah, Manju

N1 - © Springer Nature Switzerland AG 2019

PY - 2019/2/14

Y1 - 2019/2/14

N2 - Recent advances in deep learning models have demonstrated remarkable accuracy in object classification. However, the limitations of Convolutional Neural Networks such as the requirement for a large collection of labeled data for training and supervised learning process has called for enhanced feature representation and for unsupervised models.In this paper we propose a novel unsupervised sparsity-based model using Independent Subspace Analysis (ISA) to implement a hierarchical network for feature extraction. The results of our empirical evaluation demonstrates an improved classification accuracy when max pooling is paired with square pooling within each layer. In addition to accuracy, we further show that it also reduces the data dimensions within the layers outperforming known sparsity-based models.

AB - Recent advances in deep learning models have demonstrated remarkable accuracy in object classification. However, the limitations of Convolutional Neural Networks such as the requirement for a large collection of labeled data for training and supervised learning process has called for enhanced feature representation and for unsupervised models.In this paper we propose a novel unsupervised sparsity-based model using Independent Subspace Analysis (ISA) to implement a hierarchical network for feature extraction. The results of our empirical evaluation demonstrates an improved classification accuracy when max pooling is paired with square pooling within each layer. In addition to accuracy, we further show that it also reduces the data dimensions within the layers outperforming known sparsity-based models.

KW - Biologically inspired vision models

KW - Convolutional neural networks

KW - Deep learning

KW - Sparse models

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T3 - Lecture Notes in Computer Science

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EP - 505

BT - Machine Learning, Optimization, and Data Science

A2 - Nicosia , G

A2 - Pardalos, P

A2 - Giuffrida, G

A2 - Umeton, R

A2 - Sciacca, V

PB - Springer

T2 - 4th International Conference

Y2 - 13 September 2018 through 16 September 2018

ER -