我們研究了高維資料中聚類變數的選取問題，基於Chebyshev貪婪演算法(CGA)以及多元羅吉斯迴歸模型(MLR)提出了計算效率高的演算法UL-MLR來逐步選取變數，並結合了高維資訊準則(HDIC)以及修剪來加強變數選取的效果。在模擬情境下，UL-MLR具有良好的表現，相比其他聚類變數選取方法擁有更高的計算效率。我們將UL-MLR應用於四筆單細胞測序數據以及氣象資料，最終結果顯示UL-MLR選出的聚類變數能夠有效提升聚類效果。;We investigate the problem of selecting clustering variables in high-dimensional data and propose a computationally efficient algorithm, UL-MLR, based on the Chebyshev greedy algorithm (CGA) and the multinomial logistic regression model (MLR) for sequential variable selection. We incorporate a high-dimensional information criterion (HDIC) and trimming to enhance selection performance. The UL-MLR demonstrates strong performance in simulation studies and achieves higher computational efficiency than other clustering variable selection methods. We applied the UL-MLR to four single-cell sequencing datasets and meteorological data, with final results showing that the clustering variables selected by the UL-MLR effectively improved clustering performance.
