特徵向量投影 的英文怎麼說
中文拼音 [tèzhǐxiàngliángtóuyǐng]
特徵向量投影
英文
eigenvector projection- 特 : Ⅰ形容詞(特殊; 超出一般) particular; special; exceptional; unusual Ⅱ副詞1 (特別) especially; v...
- 徵 : 名詞[音樂] (古代五音之一 相當于簡譜的「5」) a note of the ancient chinese five tone scale corre...
- 量 : 量動1. (度量) measure 2. (估量) estimate; size up
- 影 : Ⅰ名詞1 (物體擋住光線后映出的形象) shadow 2 (鏡中、水面等反映出來的物體形象) reflection; image...
- 特徵 : characteristic; feature; properties; aspect; trait
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The normalized factorial moments ( nfm ) show good scaling properties in isotropical partition of phase space ; the nfm ' s projected into three directions versus the. partition. number appear, to be saturated curves with similar in scaling behavior ; and the three hurst exponents are very close to unity. the levy - stability holds for the q = 2 ~ 5 order moments in 3 - dimensional phase - space
發現:三維歸一化階乘矩( nfm )的分佈呈現出很好的標度特性;階乘矩在各個方向上的一維投影呈現出彼此十分相似的飽和曲線,且特徵參量hurst指數都接近於1 ;高維相空間各階階乘矩的分佈滿足l vy穩定性的要求。Damage degree for a structure can be detected by energy spectrum at different frequency bands for the signal decomposed by wavelet packets. 2
不同損傷狀態下的結構節點振動信號經小波包分解后在各頻帶上的投影是不同的,將其作為特徵向量可以實現對結構的損傷程度的識別。There are mainly two type of algorithms used for spatial spectrum estimation : one is those based on bayesian maximum likelihood method, like the ml ( maximum likelihood ) algorithm, maximum entropy method and etc., the others are based on the spatial decomposition or projection of correlation matrix, this kind of algorithm include vector characterization method, music ( multiple signal classification ) algorithm, projection matrix method, etc. music is a classical spatial spectrum estimation algorithm that has a super high resolution and is widely used today, however, it cannot estimate doa of signals that are correlated
空間譜估計的演算法大致分兩大類:一是基於極大似然估計和最大后驗概率估計統計理論的演算法,包括:極大似然估計法( ml ) 、最大熵法等;另一類是基於對協方差矩陣進行子空間分解或投影的演算法,包括:矢量特徵法、多重信號分類法( music ) 、投影矩陣法等。其中, music法是一種經典的空間譜估計主流演算法,具有超強的分辨性能,但它無法實現對相干信號進行測向分辨。The document space is generally of high dimensionality and clustering in such a high dimensional space is often infeasible due to the curse of dimensionality. so the primary step in document clustering is to project the document into a lower - rank semantic space in which the documents related to the same semantics are close to each other
基於文本空間的文本聚類因為其具有高維的特徵而不容易直接實現,所以文本聚類的首要步驟就是將文本空間的數據投影到較低維的語義空間里,使在文本空間里相鄰的數據向量在語義空間里根據某些提取的特徵參數而相似。This method constructs covariance matrix by utilizing data vectors in different range lines and projects phase error vector into noise sub - space which is formed by eigendecomposing the covariance matrix
該方法利用不同距離單元的觀測矢量構造協方差矩陣,然後通過對協方差矩陣特徵分解得到噪聲子空間,最後將相位誤差矢量向噪聲子空間投影來估計多普勒調頻率。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。We emphasized the selection of the eigenvector which used to create the eigenspace
針對其在組成特徵投影空間時特徵向量選擇問題的做了重點研究。The algorithm transforms 2 - dimesion image data to 1 - dimension data by projecting the character binary image on vertical and horizon direction, as to a character image of w x h size, it reduced the operation time from about w + h level to about w x h level compared to the traditional algorithm and greatly reduced the time complexity of later processing, meanwhile it saved enough vertical and horizon lattice distribution information of the character
演算法通過將字元二值化點陣圖像進行垂直與水平方向投影,實現了從二維圖像數據到一維數據的轉化,對于w h大小字元圖像,相對于傳統演算法,本演算法將運算量從w h數量級將至w + h數量級,大大降低了后續處理運算的時間復雜度,同時保留了足夠的關于字元垂直及水平點陣分佈的特徵信息。Ica as a new signal processing technique is used to determine the projection coefficient matrix which represents the features characterizing the current operating condition. multiple well - trained support vector machines use the projection coefficient matrix as their inputs to identify the fault
獨立分量分析被用來從當前工況的數據矩陣中提取出代表當前工況特徵的投影系數矩陣,而這些投影系數矩陣則被用來訓練多個支撐向量機,從而利用它們實現故障類型的識別。The outer contour of the silhouette is projected along the midline of the body to obtain the fore - and - aft midline projection vectors, which are then combined into one dimensional vector as the gait feature
將外輪廓沿人體中線投影可以得到前後兩個向量,合成1d向量( midlineprojectionvector ,中線投影向量)作為步態特徵。分享友人