no code implementations • 14 Nov 2023 • Neeraj Kumar Singh, Nikhil R. Pal
In this era of artificial intelligence, deep neural networks like Convolutional Neural Networks (CNNs) have emerged as front-runners, often surpassing human capabilities.
no code implementations • 31 Oct 2023 • Aytijhya Saha, Nikhil R. Pal
In this paper, we present a novel embedded feature selection method based on a Multi-layer Perceptron (MLP) network and generalize it for group-feature or sensor selection problems, which can control the level of redundancy among the selected features or groups.
no code implementations • 8 Jul 2023 • Suchismita Das, Nikhil R. Pal
When a data set has significant differences in its class and cluster structure, selecting features aiming only at the discrimination of classes would lead to poor clustering performance, and similarly, feature selection aiming only at preserving cluster structures would lead to poor classification performance.
no code implementations • 2 Aug 2022 • Suchismita Das, Nikhil R. Pal
Unlike traditional feature selection methods, the class-specific feature selection methods select an optimal feature subset for each class.
no code implementations • 10 Jan 2022 • Guangdong Xue, Qin Chang, Jian Wang, Kai Zhang, Nikhil R. Pal
The effectiveness of the FSRE-AdaTSK is demonstrated on 19 datasets of which five are in more than 2000 dimension including two with dimension greater than 7000.
no code implementations • 8 Apr 2020 • Suchismita Das, Nikhil R. Pal
It considers the following important issues relevant to dimensionality reduction-based data visualization: (i) preservation of neighborhood relationships, (ii) handling data on a non-linear manifold, (iii) the capability of predicting projections for new test data points, (iv) interpretability of the system, and (v) the ability to reject test points if required.
no code implementations • 26 May 2019 • Chin-Teng Lin, Kuan-Chih Huang, Yu-Ting Liu, Yang-Yin Lin, Tsung-Yu Hsieh, Nikhil R. Pal, Shang-Lin Wu, Chieh-Ning Fang, Zehong Cao
This investigation extends that study, clarifies some issues related to our earlier work, provides the algorithm for generation of the oversamples, applies the method on several benchmark data sets, and makes application to three Brain Computer Interface (BCI) applications.