Accessibility navigation

Sparse feature extraction model with independent subspace analysis

Nath, R. and Manjunathaiah, M. (2019) Sparse feature extraction model with independent subspace analysis. In: 4th International Conference, LOD 2018, 13-16 Sep 2019, Volterra, Italy, pp. 494-505.

Full text not archived in this repository.

It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing.


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 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 later. In addition to accuracy, we further show that it also reduces the data dimensions within the layers outperforming known sparsity-based models.

Item Type:Conference or Workshop Item (Paper)
Divisions:Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
ID Code:84052

University Staff: Request a correction | Centaur Editors: Update this record

Page navigation