1. Introduction
In the rapidly growing field of artificial intelligence, deep learning models, widely used in pattern recognition tasks, are excellent at extracting multidimensional features from raw data, transforming them into embeddings that reflect the complex patterns and relationships inherent in the dataset. However, the multidimensional nature of these embeddings presents a major challenge: they cannot be easily interpreted by humans without additional analysis. This comprehensibility gap requires effective dimensionality reduction and data visualization strategies, which are important for several reasons. Dimensionality reduction is crucial to overcome the "curse of dimensionality", a phenomenon where a high-dimensional feature space leads to a sparse data distribution. As the dimension increases, the volume of the space increases exponentially, making the available data too sparse to produce reliable results. This sparsity makes it difficult for algorithms to detect patterns or make predictions with high accuracy. Dimensionality reduction techniques help to overcome this curse and improve the performance and accuracy of machine learning models.
Consider multidimensional raw data described by
n features. Let us denote the data samples as
,
, where each
n-dimensional data point
, and
m is the number of data samples. Dimensionality reduction aims to find the points
,
, in a lower-dimensional space
,
, so that certain properties (such as distances or other proximities between the points) of the dataset were preserved as faithfully as possible. This dimensionality reduction is very important for interpreting data because it transforms the data into a more convenient form. If we choose
, the dimensionality reduction allows us to visualize the obtained points in 2D or 3D space. Furthermore, data visualization is crucial for understanding data in decision support systems. By transforming multidimensional data into a more comprehensible and manageable form, dimensionality reduction techniques enable decision makers to uncover hidden patterns and relationships, leading to more informed decisions. Dimensionality reduction methods can assist in identifying and understanding the unique characteristics of different data clusters, which is crucial for making informed decisions in a decision support system [
1,
2].
Often, the need for visualization arises not only in the analysis of raw data, but also in embeddings derived by deep neural networks such as Convolutional Neural Networks (CNNs). Visualization allows researchers and practitioners to gain insight into the learned representations (embeddings), contributing to a deeper understanding of model behavior. This understanding plays a key role in decision making, especially in sensitive applications such as user authentication, medical diagnosis, and autonomous driving, where fast and accurate decisions are of crucial importance. Furthermore, visualization of CNN embeddings helps to identify patterns and anomalies that may not be obvious in high-dimensional space. It allows us to explore the relationships and clusters formed by the embeddings, providing a qualitative assessment of the effectiveness of the model. For example, in user authentication using keystroke dynamics [
3], visualization of the embeddings can show how well the model discriminates between different users, which is important for assessing the reliability of an authentication system.
This paper aims to investigate the efficiency of advanced dimensionality reduction and visualization techniques in the context of analyzing multidimensional embeddings obtained by deep neural networks. The paper presents a new dimensionality reduction framework that not only improves the interpretation of multidimensional data, but also significantly enhances the comprehensibility of the data, such as in the domain of user authentication. This research is designed to deepen the understanding of complex data structures and facilitate more informed decision-making processes.
The main contributions of this paper are the following:
An innovative dimensionality reduction-based visualization framework is proposed, which uniquely integrates dimensionality reduction techniques with Siamese neural networks with a triplet loss function. It is specifically designed for interpreting and visualizing complex multidimensional embeddings from deep neural networks. It significantly enhances the interpretability of high-dimensional data, facilitating insightful and efficient analysis in decision support systems.
The usefulness and effectiveness of the proposed framework are demonstrated through an empirical evaluation using a keystroke dynamics dataset to solve a user authentication problem. This approach illustrates the ability of the framework to efficiently process and visualize multidimensional data to make reasoned decisions in user authentication systems.
The remainder of the paper is organized as follows.
Section 2 presents related works in the field of dimensionality reduction and multidimensional data visualization. The dimensionality reduction framework for multidimensional embedding visualization is outlined in
Section 3. The obtained results are presented and analyzed in
Section 4. Finally,
Section 5 summarizes the main findings and highlights future research directions.
2. Related Works
Dimensionality reduction and data visualization techniques are important in machine learning, especially when analyzing complex data [
4,
5,
6]. These methods are particularly valuable in exploratory analysis, offering insights into similarity relations in multidimensional data, which is essential for understanding and interpreting neural network embeddings.
Classical methods such as Principal Component Analysis (PCA) [
7,
8] and Multidimensional Scaling (MDS) [
8,
9,
10] have traditionally been used to reduce dimensionality in data visualization. PCA reduces the dimensionality of the data by identifying orthogonal linear combinations of the original variables (features) that have maximum variance [
11]. However, the linear PCA approach may not fully capture the complexity of nonlinear structures present in the data, which has led to the development of local distance-preserving methods such as Local Linear Embedding (LLE) [
12] and Isomap [
13,
14]. More recent methods such as t-Distributed Stochastic Neighbour Embedding (t-SNE) [
15] and Uniform Manifold Approximation and Projection (UMAP) [
16] have gained popularity due to their ability to preserve the local structure of high-dimensional data, making them particularly suitable for visualizing embeddings obtained by deep neural networks.
These techniques transform multidimensional data into a lower-dimensional space, which not only simplifies data visualization [
17,
18,
19], but also improves the computational efficiency of the tasks. In addition, visualizing the data in these reduced dimensions is very important for data interpretation.
Figure 1 (top left) shows an example of a tabular raw synthetic dataset consisting of 4050 samples in 10-dimensional space (
). The data has been generated to include four distinct clusters with additional noise and outliers. Analyzing the raw tabular data, particularly discerning interlocations and clustering, is challenging without deeper analysis. In the visualization shown at the top right, where the dimensionality of the data is reduced to three (3D space), the results are displayed in a Cartesian coordinate system, providing a clearer perspective of the data structure. While we can see the clusters of data here, they are overlapping, and it is challenging to see them in their completeness without additional interactive tools. At the bottom of the figure, visualizations where the data dimensionality is reduced to two (2D space) by three different methods (t-SNE, MDS, and PCA) are shown. Here, we can see that the clusters are best expressed using the t-SNE method, while with MDS and PCA, although the clusters are visible, the noise is also quite significant. This example demonstrates that while traditional tabular representations of multidimensional data hide complex structures, dimensionality reduction techniques can greatly improve clarity.
To demonstrate the practical application of dimensionality reduction techniques, we consider an example using keystroke dynamics data from the CMU dataset (see
Section 3.2). This dataset consists of multidimensional time series representing the password typing behavior of users, which are inherently difficult to interpret in raw form due to the high-dimensional space. In raw form, each typed password record represents a complex pattern of user typing behavior, recorded as a series of time intervals between keystrokes. The raw data is unsuitable for direct human perception because there are no patterns or relationships that can be easily interpreted (see top of
Figure 2). By applying dimensionality reduction techniques to transform multidimensional keystroke dynamics into 2D space the internal structure of the data is preserved. To illustrate, consider data associated with two separate different users, each of whom entered a password 400 times (
). Methods such as t-SNE, MDS, and PCA have been used to visualize the data in a more intuitive way. Applying these methods allows us to identify distinct clusters in the transformed data that correspond to the unique typing patterns of each user. As shown in
Figure 2, the t-SNE method in particular effectively separates data points into consistent clusters, allowing us to visually distinguish between two users. This visualization clarity demonstrates the potential of dimensionality reduction methods to authenticate users by highlighting individual keystroke dynamics. Different dimensionality reduction methods emphasize different aspects of the data. While PCA provides a broad overview by maximizing variance and identifying potential linear relationships, t-SNE and MDS focus on preserving local and global patterns, respectively. The resulting 2D visual representation of the data is more understandable and highlights the unique typing characteristics of each user, which is important for authentication purposes. While there is already some visual separation of users, decision making is complicated by overlapping clusters when there are many users. Therefore, in the remainder of this paper, a strategy to improve the visualization of clusters is proposed (see
Section 3).
The importance of dimensionality-reduction techniques for transforming multidimensional data into lower-dimensional data to facilitate easier visualization and analysis is discussed in [
20]. It focuses on the use of popular nonlinear dimensionality reduction algorithms such as t-SNE and UMAP. In the article [
21], traditional dimensionality reduction methods such as PCA and Isomap are compared with autoencoders. The study demonstrated the relevance of PCA in this context, despite the emergence of autoencoders as a more flexible neural network alternative for dimensionality reduction. While autoencoders provided nonlinear dimensionality reduction that can outperform methods such as PCA, they also required more computational time or resources for training. The findings showed that PCA remained competitive with neural networks in terms of accuracy when the projection dimensionality was large enough. However, PCA was significantly faster and less computationally intensive than autoencoders.
The paper [
22] explored the use of vibration features extracted based on machine learning models for identifying bearing faults. The possibility of reducing the number of features was examined, and the performance of the model was observed when working with reduced dimensionality of the input data. Of all the methods evaluated, PCA performed the best, second only to manual feature selection based on expert knowledge. Visual analytics for cluster analysis and dimensionality reduction of high-dimensional electronic health records was proposed and analyzed in [
23]. The main contribution of the proposed visual analytics was to bring together a wide range of state-of-the-art and traditional analysis methods, integrating them seamlessly and making them accessible through interactive visualizations.
The study [
24] investigated how different visualization methods reflect both discrete and continuous structures in single-cell data. The focus was on a new method and its comparison with UMAP. The authors used both simulated and real datasets to evaluate the performance of these methods. They aimed to find out how well these methods preserve different patterns, such as discrete clusters, continua, and branching structures in low-dimensional visualizations. The paper [
25] presented a self-supervised learning algorithm that obtained interpretable and consistent embeddings of high-dimensional recordings using auxiliary variables. It combined the concepts of nonlinear independent component analysis and contrastive learning to generate latent embeddings that were dependent on behavior and/or time. The proposed algorithm was compared with t-SNE and UMAP.
In summary, recent studies have shown the growing importance of neural networks for dimensionality reduction and data visualization, especially while preserving data structure. These techniques find applications in fields ranging from genomics to cybersecurity. The dimensionality reduction methods previously described are summarized in
Table 1. The comparative study has shown that while autoencoders and other deep learning-based techniques provide flexibility, methods such as PCA, t-SNE, LLE, and UMAP remain competitive and are suitable for balancing computational efficiency, the ability to handle specific data structures, and methodological relevance to the study objectives. The continuous development of neural network technologies requires innovative solutions to overcome the challenges of data volume, computational requirements, and interpretation. Effective dimensionality reduction and visualization techniques can have a direct impact on decision-making, problem-solving, and knowledge discovery. Further research is needed to ensure that these methods remain practical and relevant to real-world problems.
4. Results
This section describes the results obtained with the proposed framework, which demonstrate the framework’s ability to extract the necessary features and visualize complex, multidimensional data representing keystroke dynamics. The CMU dataset [
28] described in
Section 3.2 was used in the experimental investigation. Since the data did not require any additional pre-processing, they were directly encoded into the images needed to train the deep neural network by using the GADF method. The decision to employ the GADF method was based on a thorough analysis of various non-image to image transformation techniques [
32]. This analysis considered several key factors, including the ability to preserve time series characteristics and compatibility with CNN architectures. After data transformation, the Siamese neural network processed the obtained images, and training resulted in embeddings that summarized the specific typing style of each user in the multidimensional embedding space.
The experiments were conducted using public Python libraries and frameworks such as TensorFlow, Numpy, and Pandas, which are extensively employed for large-scale analysis and machine learning tasks. The computational experiments were carried out on an Apple MacBook Pro equipped with an M1 Pro chip, featuring a 10-core CPU and a 16-core GPU. While the specific duration of each experiment varied depending on the complexity of the tasks, we ensured that all experiments were conducted under controlled conditions to provide a fair comparison of the different methods evaluated.
The results depicted in
Figure 5 show the visualizations after applying different dimensionality reduction techniques to the embeddings obtained from the CMU dataset. Each color corresponds to a different user in the CMU dataset and represents individual behavioral profiles. This can be used to detect anomalies that may indicate unauthorized access attempts. PCA shows a wide spread of points but does not provide a clear view of the discrete clusters. LLE reveals some structure, but with a high degree of distortion. In the case of UMAP, it is possible to observe certain clusters, but the distinction between them is not sufficiently clear and obvious, which complicates the decision making and does not allow for making appropriate and reliable decisions. In contrast, t-SNE allows for a clear distinction between clusters and specifies the unique typing patterns of different users.
Figure 5 demonstrates the visualization results for embeddings with an initial dimensionality of 256. In order to test the hypothesis that the generation of multidimensional embeddings makes sense when their dimensionality is significantly higher than two (
), it was decided to visualize the two-dimensional embeddings without applying any dimensionality reduction technique and to compare the resulting visualizations. The number of dimensions represents the number of outputs of the Siamese neural network. The results can be seen in
Figure 6. Here, the points corresponding to the users are scattered widely, and there are no distinct clusters. A comparison of
Figure 5 and
Figure 6 justifies that embedding the data in a higher dimensional space using Siamese networks and visualizing the embeddings by dimensionality reduction techniques is meaningful.
In order to illustrate the performance of the proposed framework (see
Section 3.1), it is meaningful to compare the visualization results of the raw multidimensional data (see
Figure 4) and the multidimensional embeddings obtained by Siamese neural networks (see
Figure 5). The comparison results illustrate that multidimensional embedding visualization is more suitable for decision making, as clusters corresponding to users’ keystroke dynamics patterns are better separated and more clearly visible (see
Figure 5). As previously demonstrated, the employment of the t-SNE technique better reveals the structure of the patterns analyzed.
Figure 4d represents the visualization of the raw keystroke dynamics data using t-SNE. The clusters appear to be slightly more diffused, with some overlapping between different colors, indicating that while distinct user patterns can be observed, the separation is not as clear-cut. This poses a challenge for decision making in user authentication, as the decision boundary between different users is not clear. As a result, a decision support system may have a higher rate of misclassification, leading to potential security vulnerabilities.
Figure 5d shows the visualization of multidimensional embeddings extracted by the Siamese neural network using t-SNE. The clusters in this visualization are generally more distinct and separated from each other, with less overlap between colors. This suggests that the embeddings from the Siamese neural network provide a more refined and discriminative representation of keystroke dynamics and improve the separation between different users. Such visualization results contribute to more confident authentication decisions.
Furthermore, it is important to quantitatively evaluate how well the data in the lower-dimensional space represent the original data structure and relationships. After dimensionality reduction, data points are often clustered. The silhouette score [
34] helps determine how internally cohesive and externally separated the clusters are (see
Figure 7). This is important for understanding the effectiveness of the dimensionality reduction process. It provides insights into how well the data of the reduced dimensionality maintains the intrinsic clustering characteristics of the raw data, thereby helping to quantitatively assess the impact of the framework on the data structure.
As we can see in
Figure 7, the lowest silhouette score of 0.01 is obtained for raw multidimensional data. It suggests that the raw data does not naturally form well-defined clusters. This could be due to high dimensionality or inherent noise and variability in the data. Applying t-SNE to the raw data results in a higher silhouette score of 0.064. This improvement indicates that t-SNE helps in revealing the some underlying structure of the data, making the clusters more distinct than in the raw data. When the raw data are transformed into images, and the resulting images are used to train a Siamese neural network, the silhouette score for the multidimensional embeddings obtained at the network outputs is 0.153, which is a significant improvement over the raw data. This suggests that the embedding process effectively captures the essential features, leading to better clustering. The highest silhouette score is observed when t-SNE is applied to the multidimensional embeddings, reaching 0.190. This indicates that the combination of embedding techniques with t-SNE results in the most distinct and well-separated clusters of all analyzed data.
To evaluate the suitability of the proposed framework for decision making and to distinguish legitimate users from illegitimate users according to their typing behavior, we illustrate the application of this visualization process on two different examples, each representing a separate user from the CMU dataset. In each case, we visualize all the keystroke data samples of one user and compare them with 400 randomly selected samples of other users. In
Figure 8, the clusters of blue points represent the multiple password attempts of the selected user, while the multicolored points represent the randomly selected attempts of other users. This separation shows the similar password behavior of the selected user, whose pattern is clearly different from the other users. The same visualization approach is applied to the second example (the green cluster). Here, the user’s data samples also form a separate cluster that is distinguishable from the other users’ samples. The consistency of the results in both examples confirms the validity of the multidimensional data visualization process. The silhouette scores of 0.690 (
Figure 8, on the left) and 0.645 (
Figure 8, on the right) justify the effectiveness of the visualization framework for distinguishing between legitimate and illegitimate users based on their typing patterns using the CMU keystroke dynamics dataset. This fairly clear distinction highlights the potential of using the framework to define and visualize individual typing behavior, a critical capability for detecting unauthorized access attempts.
Author Contributions
Conceptualization, O.K., A.B. and V.M.; methodology, A.B., O.K. and V.M.; software, A.B.; validation, A.B., O.K. and V.M.; formal analysis, O.K.; investigation, A.B. and V.M.; resources, A.B.; data curation, A.B.; writing—original draft preparation, O.K., A.B. and V.M.; writing—review and editing, O.K., A.B. and V.M.; visualization, V.M.; supervision, O.K. All authors have read and agreed to the published version of the manuscript.
Funding
This research received no external funding.
Institutional Review Board Statement
Not applicable.
Informed Consent Statement
Not applicable.
Data Availability Statement
Data are available on demand.
Conflicts of Interest
The authors declare no conflicts of interest.
Abbreviations
The following abbreviations are used in this manuscript:
CNN | Convolutional Neural Network |
CMU | Carnegie Mellon University |
GADF | Gramian Angular Difference Field |
GASF | Gramian Angular Summation Field |
LLE | Local Linear Embedding |
MDS | Multidimensional Scaling |
PCA | Principal Component Analysis |
t-SNE | t-Distributed Stochastic Neighbour Embedding |
UMAP | Uniform Manifold Approximation and Projection |
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