minor component analysis

基本解釋

A minor component analysis(MCA) learning rule is presented which includes a penalty term on the self-stabilizing MCA learning rule.
基于自穩(wěn)定次元分析學習規(guī)則，提出一種添加了懲罰項的次元分析（MCA）學習規(guī)則。
Total least square(TLS) method is used to solve the linear equations of motion estimation. Then a minor component analysis (MCA) neuron is constructed to get the TLS solution.
在運動估計線性算法的基礎上，文中利用全最小二乘（TLS）方法來進行求解，并建立次分量提取神經(jīng)元來獲得該全最小二乘解。

最小主元分析
As an important tool of signal process and data analysis, MCA (minor component analysis) has been widely used in beamforming, frequency estimation, curve/surface fitting, computer graphics and many other engineering fields.
作為一種重要的信號處理和數(shù)據(jù)分析的工具,最小主元分析(MCA)已經(jīng)被廣泛的應用到了曲線/曲面擬合、數(shù)字波束形成、移動目標檢測、計算機圖形處理、頻率估計、總體最小二乘法(TLS)等眾多工程領域。
次元分析