linear subspace 中文意思是什麼
linear subspace
解釋
線性子空間-
Since the images of a human face lie in a complex subset of the image space that is unlikely to be modeled by a single linear subspace, we use a mixture of linear subspaces to model the distribution efface and non - face patterns. in the other words, we used fisher linear discriminator to project samples from a height dimensional image space to a lower dimensional feature space
對于背景復雜的人臉圖象的檢測，使用單個線性線性子空間很難準確地區分出人臉和非人臉模式，因此，本文使用混合線性子空間對人臉和非人臉樣本的分佈進行建模，在利用自組織映射神經網路標識人臉和非人臉樣本的基礎上構建一個fisher人臉檢測器。 -
On the one hand, the linear interpolation in ( x, y ) plane makes it easy to separate the three - dimensional null subspace corresponding to rigid body motions, hence what is left to do is just to compute the inverse of a symmetric definite submatrix numerically. in this way the numerical difficulty in computing general inverse can be avoided
在物理坐標系中的線性插值函數便於將三個剛體模式分離出來，從而只需計算對稱正定子陣的逆，避免了求廣義逆的數值困難；在參考坐標系中的高階插值函數則可保持原平面彈性單元的列式方式。 -
( 2 ) subspace lda algorithm based on fisher linear discriminant this paper introduced subspace lda algorithm based on fisher linear discriminant for the pca - based face recognition algorithm ca n ' t utilize the class information of train data
（ 2 ） 、基於fisher線性判別的subspacelda人臉識別由於pca演算法未能有效地利用訓練樣本類別信息，所以引入了基於fisher線性判別的subspacelda人臉識別演算法。 -
Firstly this paper introduced the principle of fisher linear discriminant function, and then present the subspace lda algorithm which project the data that is in high dimension space to eigenspace that is in low space and then maximize discriminant coefficient
本文介紹了fisher線性判別準則原理和實現過程，然後引入subspacelda方法，用pca將高維圖像數據投影到低維的特徵臉空間，再用lda最大化判別系數。 -
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。 -
It ' s a problem of great significance how to filtering a signal in joint time - frequency domain and reconstruct the time series from the time - frequency plane selected. in this thesis, the theory of joint time - frequency analysis and synthesis and time - frequency filtering is investigated. two kinds of new time - frequency domain filtering methods are proposed based on adaptive decomposition and linear subspace projection
本文在系統學習現有的各種時頻分析及其綜合演算法的基礎上，詳細研究了時頻域濾波的核心思想和基本方法，吸取了自適應時頻分析和線性子空間投影wigner - ville變換的優點，提出了的兩種新的時頻域濾波方法，並通過理論分析和數字模擬完成了相應的數值特性與應用特性研究。 -
Necessary and sufficient conditions for every closed maximal linear subspace to be strongly orthogonally complemented in banach spaces
空間上閉線性子空間強正交可補的充分必要條件 -
We proved that every closed maximal linear subspace in a banach space is strongly orthogonally complemented if and only if the space x is reflexive and strictly convex
摘要證明了閉的極大線性子空間是強正交可補的充分必要條件是，空間x是自反嚴格凸的。 -
We firstly analyze the theory of eigensystem of homograghy matrix between translation visions find the relation between its eigen - subspace and intrinsic parameters of camera. then we apply the eigen - system theory to camera self - calibration and get two methods for linear calibration. we also give its other applications
首先研究了平移視點間單應矩陣的特徵理論，找出其特徵子空間和攝像機內參數的關系；然後將特徵理論應用於攝像機自標定，得到兩種自標定的線性方法，並且給出了特徵理論的其它應用。
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