Description: 分类判别中，bayes判别的确具有明显的优势，与模糊，灰色，物元可拓相比，判别准确率一般都会高些，而BP神经网络由于调试麻烦，在调试过程中需要人工参与，而且存在明显的问题，局部极小点和精度与速度的矛盾，以及训练精度和仿真精度间的矛盾，等，尽管是非线性问题的一种重要方法，但是在我们项目中使用存在一定的局限，基于此，最近两天认真的研究了bayes判别，并写出bayes判别的matlab程序，与spss非逐步判别计算结果一致。-Classified Identifying, bayes discriminant does have a distinct advantage, with the fuzzy, gray, matter-element and extension compared to determine the exact rate will be higher in general, and the BP neural network trouble as a result of debugging, in the need to manually debug the process of participation, but also obvious problems, the local minimum point and the accuracy and speed of contradictions, as well as simulation training accuracy and precision of the conflict between, and so on, in spite of nonlinear problems is an important method, but the use of our project there are certain limitations, based on the Here, seriously the last couple of days to study the discriminant bayes and bayes discriminant of matlab to write procedures, and non-spss stepwise discriminant calculation results. Platform: |
Size: 4096 |
Author:lili |
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Description: Relief Algorithm RELIEF is considered one of the most successful algorithms for
assessing the quality of features due to its simplicity and effectiveness.
It has been recently proved that RELIEF is an online algorithm
that solves a convex optimization problem with a marginbased
objective function. Starting from this mathematical interpretation,
we propose a novel feature extraction algorithm, referred
to as LFE, as a natural generalization of RELIEF. LFE collects
discriminant information through local learning, and is solved as
an eigenvalue decomposition problem with a closed-form solution.
A fast implementation is also derived. Experiments on synthetic
and real-world data are presented. The results demonstrate that
LFE performs significantly better than other feature extraction algorithms
in terms of both computational efficiency and accuracy Platform: |
Size: 409600 |
Author:ugur ayan |
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Description: 了适应跟踪过程中目标光照条件的变化，并对目标特征进行在线更新，提出一种将局部二元模式(LBP)
特征与图像灰度信息相融合，同时结合增量线性判别分析对目标进行跟踪的算法．跟踪开始前，为了获得比较准确的目标描述，使用混合高斯模型和期望最大化算法对目标进行分割；跟踪过程中，通过蒙特卡罗方法对目标区域和背景区域进行采样，并更新特征空间参数．得到目标和背景的最优分类面；最后使用粒子滤波器结合最优分类面对目标状态进行预测．通过光照变化的仿真视频和自然场景视频的跟踪实验，验证了文中算法的有效性．-Tracking process to adapt to changes in the target lighting conditions, and the target feature for online updates, proposes a local binary pattern (LBP) features and image intensity information integration, combined with incremental linear discriminant analysis for target tracking algorithms. Track begins, in order to obtain a more accurate description of the objectives, the use of Gaussian mixture models and expectation maximization algorithm for target segmentation tracking process, through the Monte Carlo method of the target area and the background area sampled and updated feature space parameters. Get the optimal target and background classification surface finally Using Particle Filter optimal classification predict the state of the face of goal. By varying illumination simulation video and natural scenes video tracking experiment to verify the effectiveness of the proposed algorithm. Platform: |
Size: 608256 |
Author:wenping |
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Description: 局部线性判别嵌入算法，用于实现高维数据的特征提取与低维嵌入，可以很好地实现数据的降维。-Local linear discriminant embedding algorithm, used to implement the feature extraction and the low dimensional embedding of high-dimensional data, can well realize data dimension reduction.
Platform: |
Size: 19456 |
Author:夏颖 |
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Description: 该源代码是人脸识别中的二维单项局部判别有监督保持投影算法2DDSLPP，源代码下载后就可以执行，简单，易理解。-The source code is a two-dimensional face recognition supervised maintaining individual local discriminant projection algorithm 2DDSLPP, after downloading the source code can be executed, simple and easy to understand. Platform: |
Size: 561152 |
Author:李泷 |
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Description: 该源代码是人脸识别中的二维双项局部判别有监督保持投影算法BDSLPP，源代码下载后就可以执行，简单，易理解。-The source code is a two-dimensional face recognition double entry remain supervised local discriminant projection algorithm BDSLPP, after downloading the source code can be executed, simple and easy to understand. Platform: |
Size: 562176 |
Author:李泷 |
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Description: This study proposes a novel near infrared face recognition algorithm based on a combination of both
local and global features. In this method local features are extracted from partitioned images by means
of undecimated discrete wavelet transform (UDWT) and global features are extracted from the whole
face image by means of Zernike moments (ZMs). Spectral regression discriminant analysis (SRDA) is then
used to reduce the dimension of features. In order to make full use of global and local features and
further improve the performance, a decision fusion technique is employed by using weighted sum rule.
Experiments conducted on CASIA NIR database and PolyU-NIRFD database indicate that the proposed
method has superior overall performance compared to some other methods in the presence of facial
expressions, eyeglasses, head rotation, image noise and misalignments. Moreover its computational time
is acceptable for on-line face recognition systems Platform: |
Size: 1038336 |
Author:abdou |
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Description: 半监督降维算法，局部敏感判别分析，亲测有效，直接从作者主页下载-Semi-supervised dimensionality reduction algorithm, local sensitive discriminant analysis, effective pro-test, direct download the author' s home Platform: |
Size: 6144 |
Author:guomuhan |
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Search - local discriminant Algorithm