Description: 快速PCA计算方法，有效实现降维等操作，和特征选择-Fast PCA method of calculation of effective dimension reduction and other operations, and feature selection Platform: |
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Author:anan |
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Description: 这是学习LDA降维方法的核心程序，将降维后的特征就可以用于识别，这是非常好了解LDA多类降维的好程序！-This is the LDA dimension reduction methods to learn the core program will feature after dimension reduction can be used to identify that this is a very good understanding of many types of lower-dimensional LDA good program! Platform: |
Size: 4096 |
Author:liguo |
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Description: 这是MATLAB学习LSDA降维方法的核心程序，将降维后的特征用支持向量机进行训练和识别！-This is a MATLAB dimension reduction methods to study the core of LSDA process will feature after dimension reduction using support vector machine training and recognition! Platform: |
Size: 8192 |
Author:liguo |
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Description: 首先介绍了图像特征向量维度过高的问题以及图像特征降维处理。在讨论Zernike矩基
本概念以及图像Zernike矩形状特征向量表示的基础上,指出Zernike矩特征向量一般都是高维的。
在介绍主成分分析方法的基础上,指出可以将其应用到Zernike矩特征向量的降维中,并给出了降维
的处理过程。最后的实验结果证明了该方法的可行性。-Higher dimension of image feature is the critical p roblem and the dimension reduction is the most important
phase in image p rocessing. Itwas pointed out that the dimension of Zernike moments feature vector was generally high after
briefly introducing the basic concep t of the Zernike moments and the image Zernike moments shape feature vector. Based on
the p rincipal components analysis, itwas shown that the p rincipal components analysis (PCA) could be app lied in dimension
reduction of image Zernike moments feature. Meanwhile, the p rocess of the dimension reduction based on PCA was put
forward. The experimental results demonstrate the feasibility of the app lication. Platform: |
Size: 397312 |
Author:ll |
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Description: 用pca 和 lda 实现数据的降维,加快机器的特征提取的速度。-Pca and lda of data with dimension reduction, feature extraction to speed up the speed of the machine. Platform: |
Size: 3072 |
Author:江红 |
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Description: 实现了基于Gabor和LLE的人脸识别，在ORL数据库上有较好的效果-The code implement the facerecognition based on Gabor feature and LLE dimension reduction. Platform: |
Size: 2048 |
Author:韩静书 |
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Description: The Principal component analysis, is a standard technique used for data reduction in statistical pattern recognition and signal processing
A common problem in statistical pattern recognition is feature selection or feature extraction. Feature selection is a process whereby a data space is transformed into a feature space that theory has exactly same dimension as the original data space. However the transformation is designed in such a way that the data set is represented by a reduced number of “effective features” and most of the intrinsic information content of the data or the data set undergoes a dimensionality reduction.
PCA Platform: |
Size: 13312 |
Author:binu |
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Description: pca特征向量提取 利用pca的方法获取特征植及特征向量 最后可以自己根据需要降维-pca pca feature vector extraction method using characteristics of plants and to obtain the final feature vector dimension reduction can be their own as needed Platform: |
Size: 4096 |
Author:张天号 |
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Description: 一维无序随机信号的小树变换处理，小树变换即treelet，是一种有效的高维的，无序的，杂乱的数据进行降维，特征提取的方法-One-dimensional disorder random signal processing, the saplings transform treelet tree transform namely, is a kind of effective high-dimension, disorder, messy data dimension reduction, feature extraction method Platform: |
Size: 14336 |
Author:kobe |
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Description: KPCA主要在图像去噪声方面有应用。此外还可以进行特征提取，降维使用.-KPCA major noise in the image to have the application. You can also feature extraction using dimension reduction. Platform: |
Size: 2048 |
Author:lp |
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Description: The kernel based nonlinear independent component analysis, which consists of two separate steps. First,we map the data to a high dimensional feature space and perform dimension reduction to extract the effective subspace, which was achieved by kernel principal component analysis and can be considered as a pre processing step. Second, we need to adjust a linear transformation in this subspace to make the outputs as statistically independent
as possible. In this way, nonlinear ICA, a complex nonlinear problem, is
decomposed into two relatively standard procedures. Moreover, to over-
come the ill-posedness in nonlinear ICA solutions, we utilize the minimal
nonlinear distortion (MND) principle for regularization, in addition to
the smoothness regularizer. The MND principle states that we would
prefer the nonlinear ICA solution with the mixing system of minimal
nonlinear distortion, since in practice the nonlinearity in the data generation procedure is usually not very strong. Platform: |
Size: 533504 |
Author:msreddy |
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Description: 双向二维主成分分析，可用于特征提取和数据降维。-binary 2DPCA is usually used for feature extraction and dada dimension reduction Platform: |
Size: 1024 |
Author:chenhuasong |
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Description: 一种简单高效地基于压缩感知的跟踪算法。首先利用符合压缩感知RIP条件的随机感知矩对多尺度图像特征进行降维，然后在降维后的特征上采用简单的朴素贝叶斯分类器进行分类。该跟踪算法非常简单，但是实验结果很鲁棒，速度大概能到达40帧/秒-A simple and efficient tracking algorithm based on compressed sensing. Firstly, with the random sensing matrix compressed sensing RIP conditions for multi-scale image feature dimension reduction, and then use the naive Bias classifier simple classification in the feature reduction after the. The tracking algorithm is very simple, but the results are robust, speed can reach 40 frames per second Platform: |
Size: 6144 |
Author:黄明 |
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Description: 改程序勇于特征降维。使用PCA的方法降低特征维度-Courage to change the feature dimension reduction program. Use PCA method to reduce the dimension feature Platform: |
Size: 1024 |
Author:余稼祥 |
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Description: matlab中的PCA函数，主要用于人脸识别，采用的是特征降维的原理-MATLAB in the Principal Component Analysis function, mainly used for face recognition, using the principle of feature dimension reduction
Platform: |
Size: 2048 |
Author:徐玲玲 |
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Description: ：植物种类识别方法主要是根据叶片低维特征进行自动化鉴定。然而，低维特征不能全面描述叶片信息，识别准确率低，本文提
出一种基于多特征降维的植物叶片识别方法。首先通过数字图像处理技术对植物叶片彩色样本图像进行预处理，获得去除颜色、虫洞、 叶柄和背景的叶片二值图像、灰度图像和纹理图像。然后对二值图像提取几何特征和结构特征，对灰度图像提取 Hu不变矩特征、灰 度共生矩阵特征、局部二值模式特征和 Gabor 特征，对纹理图像提取分形维数，共得到 2183 维特征参数。再采用主成分分析与线性 评判分析相结合的方法对叶片多特征进行特征降维，将叶片高维特征数据降到低维空间。使用降维后的训练样本特征数据对支持向量 机分类器进行训练-plant species identification method is mainly based on blade automatic identification of low dimensional characteristics.However, can not fully describe blade low-dimensional feature information, identification accuracy is low, in this paper
A kind of plant leaves recognition method based on multiple feature dimension reduction.First by digital image processing technology to the plant leaf color sample image preprocessing, obtain background color removal, wormhole, petioles, and the blades of a binary image, gray image and texture image.Then the binary image to extract the geometric characteristics and characteristics of structure and characteristics of gray image extraction Hu moment invariants, gray co-occurrence matrix feature, local binary pattern features and Gabor, to extract the fractal dimension of texture image, get 2183 d characteristic parameters.By principal component analysis and linear uation analysis method of combining the characteristics of blade more feature dimensi Platform: |
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Author:hahah |
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Search - feature dimension reduction