Table 7.

Few of the recent works for dimensionality reduction

Ref.	Year	Area	Objective	Method used/Remarks
[161]	2021	Deep learning	proposes a hybrid approach to analyse and recognize human activity on the same dataset using deep learning methods on a cloud-based platform and principal component analysis is applied to the dataset to get the most important features.	561 features to 48 using 50 PCA components; CNN used further
[142]	2021	Sensor data	proposed a dimensionality reduction technique called fast feature dimensionality reduction technique (FFDRT)[reduces the number of features to 561 to 66]	Input features: 6000 × 561; output: 6000 × 66; classification algorithm used: KNN, random forest, deep learning
[201]	2019	CNN	a network intrusion detection model based on a convolutional neural network-IDS (CNN-IDS). Redundant and irrelevant features in the network traffic data are first removed using different dimensionality reduction methods. Features of the dimensionality reduction data are automatically extracted using CNN, and more effective information for identifying intrusion is extracted by supervised learning.	CNN-IDS, DNN, RNN
[156]	2017	Deep learning, hand-crafted features	investigates a particular approach to combine hand-crafted features and deep learning to (i) achieve an early fusion of off-the-shelf handcrafted global image features and (ii) reduce the overall number of dimensions to combine both worlds. This method allows for fast image retrieval in domains, where training data is sparse.	Reduction in processing time; handcrafted and deep learning;
[66]	2016	Machine learning	to deal with HAR modeling involving a significant number of variables in order to identify relevant parameters from data and thus maximize the classification accuracy while minimizing the number of features using data mining techniques	Dimensionality reduction models: Pristine, PCA with KNN, C5.0 as recognition models
[44]	2016	Sensor data	introduce the framework of a manifold elastic net that encodes the local geometry to find an aligned coordinate system for data representation.	PCA for dimensionality reduction
[195]	2016	Deep learning	to investigate the dimensionality reduction ability of the auto-encoder, and see if it has some kind of good property that might accumulate when being stacked and thus contribute to the success of deep learning.	Auto-encoders for dimensionality reduction
[193]	2014	Neural network	a dimensionality reduction method by manifold learning, which iteratively explores data relations and uses the relation to pursue the manifold structure.	Autoencoder used for dimensionality reduction; deep autoencoders used for complex datasets