vector subspace 中文意思是什麼
vector subspace
解釋
向量子空間-
We have discussed sum for subspace of vector space, and cannot popularize sum of subspace to infinitude
摘要對向量空間的子空間的和的概念不能推廣到無限多個子空間的情形進行了討論。 -
By projecting feature vector to every class subspace, the character can be determined to one class in accordance with the projecting length. this is the difference between subspace method and other statistic methods
在分類決策時，將樣本特徵矢量向各類別子空間投影，由投影長度判別樣本歸屬，這也是子空間方法與其它統計模式識別方法的不同之處。 -
Pca model was set up based on the normal history data of the system to detect the sensor faults by comparing the projection on the residual subspace to which the real measurement vector and normal vector were projected
主成分分析法根據系統正常的歷史數據建立數理統計模型，通過傳感器實際測量數據與正常數據在故障子空間投影的比較，對傳感器的故障進行在線檢測。 -
A construction of authentication codes with arbitration from subspace of vector space over finite fields is presented, the parameters of the code are computed
摘要利用有限域上向量空間的子空間構作了一個具有仲裁的認證碼，計算了這個碼的參數。 -
Definition2 ： given two vector spaces, if ， then v1 is called a subspace of v2
易見，任何維向量組生成的向量空間都是的子空間。 -
Some properties of separation vector of linear block codes are shown, the relationship of a variety of codewords subspace and separation vector is derived, also a serie of theorems of parity check matrix and code symbol separation are proved. the singlton bound of separation is then proved
然後簡單分析了線性碼的碼元分離度的性質，並在此基礎上分析了一致校驗矩陣的列相關性，從而得到了線性碼的消息分離度和碼元分離度的singlton限。 -
In order to obtain an unique valuation, we must add new pricing criterions, and the different pricing criterions will lead to different approximate valuation, but from the point of the theory of hilbert space, no matter what kinds of approximate valuation, they can be view as the project of a vector from the hilbert space to its closed subspace, the only difference is the different definitions of norm
為了得到唯一定價，必須引入其它定價標準，引入定價標準不同，就可以得到不同的近似定價，但從hilbert空間投影理論看，無論何種近似定價，都可以看成是hilbert空間的一個向量到它的一個閉子空間投影，差別只是所定義范數不同而已。 -
One - dimension noise subspace - based method always assumes that only the minimum eigenvalue of the signal covariance matrix is the noise eigenvalue, and the corresponding eigenvector is the true noise vector and constructs the one - dimension noise subspace
基於一維噪聲子空間方法始終認定只有信號協方差矩陣的最小特徵值才是噪聲特徵值，其對應的特徵向量才是真正的噪聲向量，並構成一維噪聲子空間。
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