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\journal{...}
\begin{document}
\begin{frontmatter}
\title{Title}

 \author{Ma Ae$^{1}$, Fa Sw$^{2}$, M E$^{3,}$\footnote{Corresponding author: m.e@abcd.ac.ir (M. E)}}

 \address{$^{1}$Department of ..., Faculty of ..., University of ..., City, Country}
\address{$^{2}$Department of ..., Faculty of ..., University of ..., City, Country}
\address{$^{3}$Department of ..., Faculty of ..., University of ..., City, Country}
\begin{abstract}
Please write Abstract here.
\end{abstract}

 \begin{keyword}
Key 1, Key 2, Key 3, Key 4, Key 5.
\end{keyword}
\end{frontmatter}
\linenumbers

 \section{Introduction}\label{S:1}
Please write Introduction here.

 This paper introduces .... In detail, the primary contributions of the proposed method can be summarized as follows:
\begin{itemize}
\item
Item 1

 \item
Item 2

 \item
Item 3
\end{itemize}

 Following is the outline of the organization of this paper. Section \ref{motsec} explains ...


\section{Motivation or Related Work}\label{motsec}
Please write Motivation or Related Work here.

 \section{Methodology}\label{regsec}
There are various subspace distances that have been proposed and utilized for feature selection tasks. One of the commonly used subspace distances for feature selection is the directional distance \cite{moslemi2023,Garcia2010}, $\mathrm{d}_{\mathrm{dist}}$, which is defined as:
\begin{equation}\label{for1}
\mathrm{d}_{\mathrm{dist}}(\mathrm{span}(\mf{A}),\mathrm{span}(\mf{A}_{I}))=\min_{\mf{H}\in\mathbb{R}^{k\times d}} \|\mf{A}-\mf{A}_{I}\mf{H}\|_{F},
\end{equation}
where $A_I=[\mf{f}_{i_1},\ldots,\mf{f}_{i_k}]$ is a given submatrix of $k$ features, and $\mf{H}$ is the coefficient representation matrix.

 The directional distance problem \eqref{for1} declares that the goal is to minimize the distance between the subspace formed by the chosen features and the subspace formed by the entire set of features.

 {\renewcommand{\baselinestretch}{1}
\begin{algorithm}[!h]
\caption{Iterative algorithm for the proposed method.}
\label{alg1}
\begin{algorithmic}[1]
\REQUIRE
\STATEx $\mf{A}\in\mathbb{R}^{n\times d}$: The data matrix with $n$ data samples and $d$ features;
\STATEx $k$: the number of selected features;
\STATE Initialize $\mf{W}$ as a random matrix of size $\mathbb{R}^{d\times k}$ and $\mf{H}$ as a random matrix of size $\mathbb{R}^{k\times p}$.
\WHILE{iteration $\leq$ Iter\_{max}}
\STATE Update $\mf{W}$ by ....
\STATE Update $\mf{H}$ by ....
\ENDWHILE
\ENSURE Choose a set of $k$ features as the output of the feature selection method.
\end{algorithmic}
\end{algorithm}}


\subsection{Computational complexity of the proposed method}\label{hfcmmatrix1}



 \subsection{Convergence analysis of the proposed method}\label{hfcmmatrix}


\section{Experimental evaluations}\label{s2}
This section contains a description of several experiments conducted to examine the effectiveness and characteristics of the proposed method.

 \subsection{Datasets}
The evaluation phase of the experiments utilizes ten distinct datasets that are publicly available. These datasets include ...


\begin{table}[!h]
	\centering
	\caption{Overview of the datasets utilized in this study, in which $n$ refers to the number of data samples, $d$ refers to the number of features, and $c$ is the number of distinct classes.}
\scalebox{0.9}{\begin{tabular}{|l||ccc|l|}
		\hline
		\multicolumn{1}{|c||}{\textbf{Dataset}} & $n$ & $d$ & $c$ & \multicolumn{1}{c|}{\textbf{Type of Data}}\\
		\hline
	 ALLAML & 72 & 7129 & 2 & Biological microarray \\
	 Orlraws10P & 100 & 10304 & 10 & Face image\\
		BASEHOCK & 1993 & 4862 & 2 & Text\\
		\hline
	\end{tabular}}
	\label{tab1}
\end{table}



 \subsection{Comparison methods}
The effectiveness of the proposed method is examined by comparing it to some previous and representative unsupervised feature selection methods. The following list summarizes the methods tested in the experiments.

 \begin{enumerate}
\item
Baseline utilizes all features directly without feature selection.

 \item LS \cite{he2005laplacian} performs the feature selection process by considering both the variance information and the geometric of data to calculate the Laplacian score of features.

 \item MCFS \cite{cai2010unsupervised} is a spectral regression model based on the $l_1$-norm regularization.

 \end{enumerate}




\subsection{Parameter settings and evaluation metrics}\label{sec33}
As some regularization parameters exist in the comparison methods and our proposed method, it is essential to indicate their values.


\subsection{Results and analysis}






\subsection{Analyzing the sensitivity of parameters}
To examine the influence of the regularization parameters $\alpha$, $\beta$, $\lambda$ and $\rho$ on the clustering performance of our proposed method, a comprehensive investigation is conducted in the following. See Figure \ref{fig1}.

 \begin{figure}[!htbp]
	\centering
	\includegraphics[scale=0.4]{pic1}
	\caption{Caption}
	\label{fig1}
\end{figure}

 \subsection{Ablation study}
The proposed method integrates the three regularization constraints:


\subsection{Test for statistical analysis}
\subsection{Computational cost of various methods}
This section presents a comparative analysis of the computational evaluations of the proposed method with other approaches.

 \begin{table}[!h]
	\centering
	\caption{Comparing computational cost of various methods. Here, $n$ and $d$ correspond respectively to the number of data samples and the number of features.}
	\scalebox{0.7}{\begin{tabular}{|l||l|}
			\hline
			\textbf{Method} & \multicolumn{1}{c|}{\textbf{Computational complexity}} \\
			\hline
			LS & $O(dn^2)$ \\
			MCFS & $O(dn^{2})$ \\
			\hline
	\end{tabular}}
	\label{tab2}
\end{table}



 \subsection{Testing the convergence behavior of ...}
The experimental study of this section focuses on the convergence behavior of the proposed method for the datasets utilized in the experiments.

 \begin{figure}[!htbp]
	\centering
		\begin{subfigure}
			\centering
			\includegraphics[scale=0.3]{O1}
			\quad
			\includegraphics[scale=0.3]{O2}
		\end{subfigure}
	\caption{Objective function curves across various datasets with respect to the number of iterations.}
	\label{fig2}
\end{figure}


\section{Conclusion}\label{s3}
Please write Conclusion here.

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\end{document} 
