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Adaptive Feature Selection and Image Classification Using Manifold Learning Techniques
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DOI：10.1109/ACCESS.2023.3322147.png)
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Abstract
En 中文
Manifold learning techniques aim to the non-linear dimension reduction of data. Dimension reduction is the field of interest and demand of many data analysts and is widely used in computer vision, image processing, pattern recognition, neural networks, and machine learning. The research has been divided into two phases to recognize manifold learning techniques' importance. In the first phase, the manifold learning approach is used to improve the 'feature selection by clustering'. Clustering algorithms such as K-means, spectral clustering, and the Gaussian Mixer Model have been tested with manifold learning approaches for adaptive feature selection. The results obtained are satisfactory compared to simple clustering. In the second phase, a Triple Layered Convolutional Architecture (TLCA) has been proposed for image classification bearing 85.34%, 59.14%, 71.43%, 90.06%, and 71.71% accuracy levels for the datasets such as Pistachio, Animal, HAR, Mango Leaves, and Cards respectively. The performance of the proposed TLCA model is compared to the other deep learning models i.e., CNN, LSTM, and GRU. To further improve the accuracy, reduced dimensional data from manifold learning technique is used and achieved higher accuracies from Hybrid Triple Layered Convolutional Architecture HTLCA as 97.73%, 87.18%, 97.97%, 99.19%, and 96.91% for the mentioned sequence of datasets. The effectiveness and precision of the suggested methods are demonstrated by the experimental findings.
Keywords:
Clustering
feature extraction
feature selection triple layered convolutional architecture
Journal
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3.6
Papers:
9.8W
Citations:
29.4W