\section{Analysis of silhouette coefficient}

In this section, we evaluated how normalization could leverage numerical and visual data segregation applied in the main paper text. 
We compared silhouette coefficient of original space, normalized space (\emph{Min-max} normalization), and the projected space (with original feature values).
Features were divided into four groups: acoustic indices and other acoustic measurements, standard image descriptors,
complete autoencoder (23,040 features per spectrogram) and summarized autoencoder (160 summarized features per
spectrogram).

Table~\ref{tab:silhouette_original} shows silhouette of the original space described by features with and without normalization. 
Moreover, figure presents values that consider 5 classes (pointing out Ilh{\'e}us as 2 classes) and 4 classes (pointing out Ilh{\'e}us as 1 class). 
Values helped to decide to use normalized feature. 

Table~\ref{tab:silhouette_caracteristica_projecao} presents silhouette values (feature without normalization) used to analyze the relationship between features and projections. 

Table~\ref{tab:silhouette_data_acoustic_features} presents silhouette values considering acoustic features divided into indices, MFCC, and other measurements.  
Meanwhile, Table~\ref{tab:silhouette_data_image_features} shows silhouette considering texture and color descriptors.
Each feature set has values from original (without normalization), normalized, and projected spaces (t-SNE). 
The projections silhouette correspond to image projections presented in the main paper text.

\begin{table}[!htb]
    \centering
    \begin{tabular}{l|c|c|c|c} 
    \toprule
                                       & \multicolumn{2}{c|}{\textbf{5 Classes}} & \multicolumn{2}{c}{\textbf{4 Classes}} \\ \midrule
    \textbf{Data}                      & \textbf{Original} & \textbf{Normalized} & \textbf{Original} & \textbf{Normalized} \\ \midrule
    \multicolumn{5}{c}{Acoustic indices and other acoustic measurements}                                           \\ \midrule  
    \textit{Costa Rica}                 & 0.01              & 0.06   & 0.01 & 0.05            \\ 
    \textit{Ilh{\'e}us}                     & 0.04              & 0.03  & -- &     --        \\ 
    \textit{\textbf{Costa Rica + Ilh{\'e}us + Laje}} & \textbf{-0.13}              & \textbf{0.12}     & \textbf{-0.07} &    \textbf{0.20}      \\ \midrule
    \multicolumn{5}{c}{Image Descriptors}                                       \\ \midrule
    \textit{Costa Rica}                 & 0.07              & 0.05     & 0.07 &  0.05        \\ 
    \textit{Ilh{\'e}us}                     & 0.07              & 0.05     & -- &    --      \\ 
    \textit{\textbf{Costa Rica + Ilh{\'e}us + Laje}}        & \textbf{0.10}              & \textbf{0.09}     &  \textbf{0.12} &    \textbf{0.10}      \\ \midrule
    \multicolumn{5}{c}{Complete Autoencoder}                                       \\ \midrule
    \textit{Costa Rica}                 &     0.03         &  0.03  & 0.03 & 0.03 \\ 
    \textit{Ilh{\'e}us}                     &  0.04        &      0.03   & -- & --  \\ 
    \textit{\textbf{Costa Rica + Ilh{\'e}us + Laje}}        &   \textbf{0.02} & \textbf{0.02}    & \textbf{0.08} & \textbf{0.08} \\ \midrule
    \multicolumn{5}{c}{Summarized Autoencoder}                                       \\ \midrule
    \textit{Costa Rica}                 &    0.04         &     0.04    & 0.04 &  0.04      \\ 
    \textit{Ilh{\'e}us}                     &   0.04            &   0.04    & -- &  --       \\ 
    \textit{\textbf{Costa Rica + Ilh{\'e}us + Laje}}        & \textbf{0.01}             &     \textbf{0.01}    & \textbf{0.09} &  \textbf{0.08}     \\ 
    \bottomrule
    \end{tabular}
    \caption{Silhouette values from feature sets considering 5 classes (Ilh{\'e}us as 2 classes) and 4 classes (Ilh{\'e}us as 1 class). Calculations considered original space with and without normalization.
    The best value in each column is highlighted.}
    \label{tab:silhouette_original}
  \end{table}

  \begin{table}[!htb]
    \centering
    \begin{tabular}{lcccccc} 
    \toprule
    & \textbf{Force} & \textbf{LAMP}    & \textbf{LSP}     & \textbf{MDS}     & \textbf{PCA}     & \textbf{t-SNE}    \\ 
    \midrule
    \textit{Indices and Measurements}                               & -0.1412     & -0.153 & -0.1254 & -0.1468 & -0.1468 & {\color{blue}\textbf{0.0264}} \\
    \midrule
    \textit{Image Descriptors}                           & 0.0502      & 0.0805  & {\color{blue}\textbf{0.0882}}  & 0.0485  & 0.0485  & 0.0518   \\ 
    \midrule
    \textit{Complete Autoencoder}   & 0.0608 & 0.0557 & {\color{blue}\textbf{0.0690}}  & 0.0643 & 0.0643 & 0.0220    \\ 
    \midrule
  \textit{Summarized Autoencoder} &  0.0705 & 0.0717  & {\color{blue}\textbf{0.0736}} & 0.0696  & 0.0696 & -0.0237  \\                                              
  \bottomrule
    \end{tabular}
\caption{Silhouette values from feature sets associated with MDP. 
Feature values were not normalized. 
The best values of each row are highlighted. 
The complete autoencoder values has all features (23,040 values) generated from autoencoder technique and \textit{summarized} has a statistical summary (160 values) of them. }
    \label{tab:silhouette_caracteristica_projecao}      
  \end{table}
  
\begin{table}[!htb]
\centering
\begin{tabular}{lccccccccc}
\toprule
                           & \multicolumn{3}{c}{\textbf{Indices}}       & \multicolumn{3}{c}{\textbf{MFCC}}          & \multicolumn{3}{c}{\textbf{\begin{tabular}[c]{@{}c@{}}Other\\ Measurements\end{tabular}}} \\
\cmidrule(l){2-4}                          
\cmidrule(l){5-7}                      
\cmidrule(l){8-10}
                           & \textbf{Original} & \textbf{Norm.} & \textbf{Proj.} & \textbf{Original} & \textbf{Norm.} & \textbf{Proj.} & \textbf{Original}   & \textbf{Norm.}   & \textbf{Proj.}  \\
\cmidrule(l){2-4}                          
\cmidrule(l){5-7}                      
\cmidrule(l){8-10}                 
\textit{Costa Rica}                 &     0.0002	     &   0.0187         &   0.0162        &          0.0948 &  0.0681          &   0.2216      &     0.0099       &      0.0567        &      0.0412       \\
\textit{Ilh{\'e}us}                     &   0.0373       &   0.0395         &    0.0510       &  0.0255         &    0.0240        &    0.0082       &   0.0395         &    0.0102          &    0.0143        \\
\textit{\textbf{\begin{tabular}[c]{@{}c@{}}Costa Rica + \\ Ilh{\'e}us + Laje\end{tabular}}} &   \textbf{0.1587}       &    \textbf{0.0981}        &    \textbf{0.0119}       &    \textbf{0.1842}      &     \textbf{0.1389}       &   \textbf{0.1121}        &   \textbf{-0.1905}         &    \textbf{0.0091}          &  \textbf{0.0330}   \\           
\bottomrule
\end{tabular}
\caption{Silhouette values from acoustic features divided into 3 feature sets. 
Columns of each set present silhouette values from original, normalized, and projected space (t-SNE). 
Values were calculated with 5 classes (Ilh{\'e}us as 2 classes).
The best value in each column is highlighted.}
\label{tab:silhouette_data_acoustic_features}
\end{table}  



\begin{table}[]
\centering
\begin{tabular}{lcccccc}
\toprule
                           & \multicolumn{3}{c}{\textbf{Texture}}       & \multicolumn{3}{c}{\textbf{Color}}         \\
\cmidrule(l){2-4}                          
\cmidrule(l){5-7}
                           & \textbf{Original} & \textbf{Normalized} & \textbf{Projected} & \textbf{Original} & \textbf{Normalized} & \textbf{Projected} \\
\cmidrule(l){2-4}                          
\cmidrule(l){5-7}                  
\textit{Costa Rica}                 &  0.0282        &  0.0246          &    0.0254       &   0.0742       &      0.0699      &     0.1003      \\
\textit{Ilh{\'e}us}                     &  0.0868        &  0.0456          &    0.0373       &   0.0685       &   0.0590         &    0.0613       \\
\textit{\textbf{Costa Rica + Ilh{\'e}us + Laje}} &  \textbf{0.1157}        &   \textbf{0.1036}         &    \textbf{0.0412}       &    \textbf{0.0939}      &    \textbf{0.0811}        &  \textbf{0.0602} \\        
\bottomrule
\end{tabular}
\caption{Silhouette values from image descriptors divided into 2 feature sets. 
Columns of each set present silhouette values from original, normalized ,and projected space (t-SNE). Values were calculated with 5 classes (Ilh{\'e}us as 2 classes). 
The best value in each column is highlighted.}
\label{tab:silhouette_data_image_features}
\end{table}

\clearpage
\section{Acoustic indices and other acoustic measurements} 

This section helped to understand how \emph{Costa Rica} dataset is represented with different acoustic feature sets associated with different projections. 
Next figures just use \emph{Costa Rica} data, because it is the largest set and it presents good segregation of acoustic features in the main paper text.   

Figures~\ref{fig:costarica_indices}, \ref{fig:costarica_mfcc}, and~\ref{fig:costarica_othermeasurements} use 3 groups of measurements: acoustic indices (ACI, ADI, AEI, BIO, H, Hf, Ht NDSI, AR and M), 12 MFCC values, and other acoustic measurements (SPL, RMS, \textit{Rugosity}, \textit{Roughness} and \textit{Number of Peaks}). 
We figured out which features are the best ones to describe our data, with these tests.
 
\begin{figure}[!htb]
\centering
\includegraphics[scale=0.40]{img/costarica_indices}
\caption{Projections of 3,061 recordings using 9 \textbf{acoustic indices}, and normalized with \textit{Minmax} between 0 and 1. Colors indicate each collection site in Costa Rica (black and red).}
\label{fig:costarica_indices}    
\end{figure}       
  
\begin{figure}[!htb]
\centering
\includegraphics[scale=0.40]{img/costarica_mfcc}
\caption{Projections of 3,061 recordings using 12 \textbf{MFCC} values, and normalized with \textit{Minmax} between 0 and 1. Colors indicate each collection site in Costa Rica (black and red).}
\label{fig:costarica_mfcc}    
\end{figure}  
 
\begin{figure}[!htb]
\centering
\includegraphics[scale=0.45]{img/costarica_others}
\caption{Projections of 3,061 recordings using 4 \textbf{acoustic measurements}, and normalized with \textit{Minmax} between 0 and 1. Colors indicate each collection site in Costa Rica (black and red).}
    \label{fig:costarica_othermeasurements}    
  \end{figure}  

\clearpage
\section{Standard image descriptors tests} 

This section helped to understand how \emph{Costa Rica} dataset is represented with different image feature sets associated with different projections. 
Next images just use \emph{Costa Rica} because it is the largest set and it presents good segregation of acoustic features in the main paper text.

Figures~\ref{fig:costarica_texture} and~\ref{fig:costarica_color} use 2 groups of descriptors: texture (GLCM, LBP, Fourier) and color (CCV, GHC, BIC, ACC). With these tests was possible to figure out which features are the best ones to describe our data.

\begin{figure}[!htb]
\centering
\includegraphics[scale=0.45]{img/descriptors_costarica_textura.png}
\caption{Projections of 3,061 recordings using 24 values of texture descriptors, and normalized with \textit{Minmax} between 0 and 1. Colors indicate each collection site in Costa Rica (black and red).}
\label{fig:costarica_texture}
\end{figure}

\begin{figure}[!htb]
\centering
\includegraphics[scale=0.45]{img/descriptors_costarica_cor.png}
\caption{Projections of 3,061 recordings using 576 values of color descriptors, and normalized with \textit{Minmax} between 0 and 1. Colors indicate each collection site in Costa Rica (black and red).}
\label{fig:costarica_color}
\end{figure}

\clearpage
\section{Unsupervised learned image descriptors tests} 

Next sections show the first tests performed to verify the capability of autoencoder to generate a feature set suitable for segregate images.
Firstly, we have split images into 20 sub-images and trained the autoencoder.
Secondly, we have taken sub-images from some spectrograms to exemplify the reconstruction and the feature spaces generated (see tests from 1 to 4).
Autoencoder was trained and extracted 1,152 from each sub-image.
We summarized the features with 8 measures: minimum and maximum values, mean, standard deviation, first quartile, median, third quartile, and interquartile range.  
\emph{Force Scheme} projection was employed to present the feature spaces. 
Finally, we have attempted to describe the results of complete spectrograms.
Instead of taking features of each sub-images, we combined them to represent a complete spectrogram and performed tests to visualize the feature space.

\subsection{Test 1}
\label{sec:test1}

Figure~\ref{fig:pre} shows a spectrogram from \emph{Costa Rica} audio file that contains patterns of \textbf{insect} sounds.
Figure~\ref{fig:rec} depicts the poorly reconstruction of the spectrogram sub-images. 
Figure~\ref{fig:slice} represents the feature space generated and we have figured out that groups pointed out are related to the rows of sub-images from Figure~\ref{fig:pre}. 

\begin{figure}[!htb]
\centering
\includegraphics[scale=0.20]{img/preprocess.png}
\caption{Spectrogram pre-processing example. The spectrogram (left) was divided into 20 sub-images (right).}
\label{fig:pre}    
\end{figure}   

\begin{figure}[!htb]
\centering
\includegraphics[scale=0.50]{img/reconstruction.png}
\caption{Reconstruction of spectrogram sub-images from
Figure~\ref{fig:pre}. Each image is named between s01 and s20.}
\label{fig:rec}
\end{figure}   

\begin{figure}[!htb]
\centering
\includegraphics[scale=0.40]{img/test_slices_a.png}
\caption{
\textit{Force Scheme} projection of sub-images (s01 to s20) from spectrogram in Figure~\ref{fig:pre}. The images use 1,152 features (left) and 8  statistical summarization of the features (right). Clusters highlighted in green represent each row of spectrogram used.}
\label{fig:slice}    
\end{figure}
  
\clearpage
\subsection{Test 2} 

Figure~\ref{fig:pre2} shows a spectrogram from \emph{Costa Rica} audio file that contains patterns of \textbf{birds} sounds.
Figure~\ref{fig:rec2} depicts the poorly reconstruction of the spectrogram sub-images. 
Figure~\ref{fig:slice2} represents the feature space generated and we have figured out that groups pointed out are related to the rows of sub-images from Figure~\ref{fig:pre2}. 

\begin{figure}[!htb]
\centering
\includegraphics[scale=0.20]{img/preprocess_b.png}
\caption{Spectrogram pre-processing example. The spectrogram (left) was divided into 20 sub-images (right). }  
\label{fig:pre2}    
\end{figure}   

  \begin{figure}[!htb]
    \centering
    \includegraphics[scale=0.50]{img/reconstruction_b.png}
    \caption{Reconstruction of spectrogram sub-images from
    Figure~\ref{fig:pre2}. Each image is named between s01 and s20.}
    \label{fig:rec2}
  \end{figure}   

  \begin{figure}[!htb]
    \centering
    \includegraphics[scale=0.40]{img/test_slices_b.png}
    \caption{\textit{Force Scheme} projection of sub-images (s01 to s20) from spectrogram in Figure~\ref{fig:pre2}. The images use 1,152 features (left) and 8  statistical summarization of the features (right). Clusters highlighted in green represent each row of spectrogram used.}
    \label{fig:slice2}    
  \end{figure}


\clearpage
\subsection{Test 3} 

Figure~\ref{fig:pre3} shows a spectrogram from \emph{Costa Rica} audio file that contains patterns of \textbf{birds} sounds.
Figure~\ref{fig:rec3} depicts the poorly reconstruction of the spectrogram sub-images. 
Figure~\ref{fig:slice3} represents the feature space generated and we have figured out that groups pointed out are related to the rows of sub-images from Figure~\ref{fig:pre3}.


\begin{figure}[!htb]
\centering
\includegraphics[scale=0.20]{img/preprocess_c.png}
\caption{ Spectrogram pre-processing example. The spectrogram (left) was divided into 20 sub-images (right).}
\label{fig:pre3}    
\end{figure}   

  \begin{figure}[!htb]
    \centering
    \includegraphics[scale=0.50]{img/reconstruction_c.png}
    \caption{Reconstruction of spectrogram sub-images from
    Figure~\ref{fig:pre3}. Each image is named between s01 and s20.}
    \label{fig:rec3}
  \end{figure}   

  \begin{figure}[!htb]
    \centering
    \includegraphics[scale=0.40]{img/test_slices_c.png}
    \caption{\textit{Force Scheme} projection of sub-images (s01 to s20) from spectrogram in Figure~\ref{fig:pre3}. The images use 1,152 features (left) and 8  statistical summarization of the features (right). Clusters highlighted in green represent each row of spectrogram used.}
    \label{fig:slice3}    
  \end{figure}

\clearpage
\subsection{Test 4} 

Figure~\ref{fig:pre4} shows a spectrogram from \emph{Ilh{\'e}us} audio file that contains patterns of \textbf{fish chorus and humpback whale sound}.
Figure~\ref{fig:rec4} depicts the poorly reconstruction of the spectrogram sub-images. 
Figure~\ref{fig:slice4} represents the feature space generated and we have figured out that groups pointed out are related to the rows of sub-images from Figure~\ref{fig:pre4}.

\begin{figure}[!htb]
    \centering
    \includegraphics[scale=0.20]{img/preprocess_d.png}
    \caption{Spectrogram pre-processing example. The spectrogram (left) was divided into 20 sub-images (right).}  
    \label{fig:pre4}    
  \end{figure}   

  \begin{figure}[!htb]
    \centering
    \includegraphics[scale=0.50]{img/reconstruction_d.png}
    \caption{Reconstruction of spectrogram sub-images from
    Figure~\ref{fig:pre4}. Each image is named between s01 and s20.}
    \label{fig:rec4}
  \end{figure}   

  \begin{figure}[!htb]
    \centering
    \includegraphics[scale=0.40]{img/test_slices_d.png}
    \caption{\textit{Force Scheme} projection of sub-images (s01 to s20) from spectrogram into Figure~\ref{fig:pre4}. The images use 1,152 features (left) and 8  statistical summarization of the features (right). Clusters highlighted in green represent each row of spectrogram used.}
    \label{fig:slice4}    
  \end{figure}
  
\clearpage
\subsection{Test 5} 

In this test, ten images, showed in Figure~\ref{fig:autoencoder_espectrogramas}, where divided into 4 groups. Figure~\ref{fig:autoencoder_teste2} presents the result of \textit{Force Scheme} projection of these images using the features generated by Autoencoder.  

Figure~\ref{fig:autoencoder_espectrogramas} shows 10 spectrograms that we nominated them as CR08, CR11, CR10, CR09, CR1302, CR12, OP4, OP3, CR1306, CR06, being CRs from \emph{Costa Rica} and OPs from \emph{Ilh{\'e}us}. 
Each row in that figure represents one group of spectrograms with distinct patterns.

Figure~\ref{fig:autoencoder_teste2} depicts projection based on features extracted with the autoencoder.

  \begin{figure}[!htb]
    \centering
    \includegraphics[scale=0.35]{img/test_groups_10_images.png}
    \caption{
    Each row represents a group of spectrograms with distinct patterns.
    }
    \label{fig:autoencoder_espectrogramas}  
  \end{figure}
  
    \begin{figure}[!htb]
    \centering
    \includegraphics[scale=0.40]{img/test_10_images.png}
    \caption{\textit{Force Scheme} projection of 10 spectrograms.
     The projection used 23,040 features (left) and 160 statistical summarization of the features (right). Clusters highlighted in green represent each row of Figure~\ref{fig:autoencoder_espectrogramas}. 
     }  
    \label{fig:autoencoder_teste2}  
  \end{figure}
  
\clearpage
\section{Automatic feature selection results} 

The Weka 3.8 data mining program\footnote{\url{https://www.cs.waikato.ac.nz/ml/weka/}} was used to select features with the aim of to compare with paper results. 
The following available algorithms were applied: \emph{Correlation based}, \emph{Information Gain} and \emph{Relief-F}. 
The selection was performed with each characteristic groups (acoustic indices and measurements,
image descriptors and image characteristics extracted by autoencoder) and assess with silhouette coefficient and MDP. 
All features where normalized with \textit{Min-max} before feature selection. 
The selections were applied to the complete dataset with 4,340 items (CostaRica, Ilh{\'e}us and Laje).

Each selection algorithm generates an ordered list of features. 
We have performed tests with incremental sets of characteristics, i.e. , first 2 attributes from each list, first 3 attributes, first 4 attributes etc.).
Therefore, with each number of features we calculated the silhouette coefficient and generated a projection.

\subsection{Acoustic indices and measurements}

Figure~\ref{fig:selection_indice} presents silhouette generated with each number of features.
The first 6 attributes returned by \emph{Correlation based} generated the greatest silhouette value (0.2526): ACI, ADI, BIO, H, MFCC\_2, MFCC\_4, MFCC\_12. Figure~\ref{fig:selection_indice_tsne} presents the t-SNE  projection using these features.

  \begin{figure}[!htb]
    \centering
    \includegraphics[scale=0.2]{img/selection_indice_silhouette.png} 
    \caption{Silhouette values generated with lists of features generated by distinct feature selection techniques. 
    On the left, there are 4 labels with Ilh{\'e}us areas joined. 
    On the left, there are 5 labels with Ilh{\'e}us areas separated.}
    \label{fig:selection_indice}    
  \end{figure}    
  
  \begin{figure}[!htb]
    \centering
    \includegraphics[scale=0.35]{img/selection_indice_tsne.png} 
    \caption{t-SNE projection using 6 acoustic indices and measurements selected with \emph{Correlation based} algorithm. }
    \label{fig:selection_indice_tsne}    
  \end{figure}

\clearpage
\subsection{Image descriptors}

Figure~\ref{fig:selection_descriptor} presents silhouette generated with each number of features.
The first 2 attributes returned by \emph{Correlation based} generated the greatest silhouette value (0.1638): LBP9, Contrast of co-ocurrence matrix. 
Figure~\ref{fig:selection_descriptor_tsne} presents the t-SNE  projection using these features.

  \begin{figure}[!htb]
    \centering
    \includegraphics[scale=0.2]{img/selection_descriptor_silhouette.png} 
    \caption{Silhouette values generated with lists of features generated by distinct feature selection techniques. 
    On the left, there are 4 labels with Ilh{\'e}us areas joined. 
    On the left, there are 5 labels with Ilh{\'e}us areas separated.}       
    \label{fig:selection_descriptor}    
  \end{figure}   
    
  \begin{figure}[!htb]
    \centering
    \includegraphics[scale=0.35]{img/selection_descriptor_tsne.png} 
    \caption{t-SNE projection using 2 image descriptors selected with \emph{Correlation based} algorithm. }    
    \label{fig:selection_descriptor_tsne}    
  \end{figure}  

\clearpage
\subsection{Image characteristics generated by autoencoder}

Figures~\ref{fig:selection_autoencoder23040_silhouette} and~\ref{fig:selection_autoencoder160_silhouette} present silhouette generated with each number of features.
The former presents all features (23,040 values) and the latter presents summarized features (160 values).

For 23,040 features, the first 48 attributes returned by \emph{Relief-F} generated the greatest silhouette value (-0.0180). For 160 features, the first 58 attributes returned for \emph{Relief-F} generated the greatest silhouette value (-0.0634).
Figures~\ref{fig:selection_autoencoder23040_tsne} and~\ref{fig:selection_autoencoder160_tsne} present the t-SNE  projection of a test set of recordings.
The former has the complete feature space and the latter the summarized feature space.

 \begin{figure}[!htb]
    \centering
    \includegraphics[scale=0.2]{img/selection_autoencoder23040_silhouette.png} 
    \caption{Silhouette values calculated with lists of features generated by distinct feature selection techniques.
    Complete feature set was considered (23,040 values).
    On the left, there are 4 labels with Ilh{\'e}us areas joined. 
    On the left, there are 5 labels with Ilh{\'e}us areas separated.}    
    \label{fig:selection_autoencoder23040_silhouette}    
  \end{figure}   
  
 \begin{figure}[!htb]
    \centering
    \includegraphics[scale=0.2]{img/selection_autoencoder160_silhouette.png} 
    \caption{Silhouette values calculated with lists of features generated by distinct feature selection techniques.
    Summarized feature set was considered (160 values).
    On the left, there are 4 labels with Ilh{\'e}us areas joined. 
    On the left, there are 5 labels with Ilh{\'e}us areas separated.}    
    \label{fig:selection_autoencoder160_silhouette}    
  \end{figure}     
  
  \begin{figure}[!htb]
    \centering
    \includegraphics[scale=0.35]{img/selection_autoencoder23040_tsne.png} 
    \caption{t-SNE projection using 48 features selected from the complete feature set, with \emph{Relief-F} algorithm. }      
    \label{fig:selection_autoencoder23040_tsne}    
  \end{figure} 
  

  \begin{figure}[!htb]
    \centering
    \includegraphics[scale=0.35]{img/selection_autoencoder160_tsne.png} 
    \caption{t-SNE projection using 58 features selected from the summarized feature set, with \emph{Relief-F} algorithm. }     
    \label{fig:selection_autoencoder160_tsne}    
  \end{figure}      
 