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Nonlinear dimensionality reduction by locally linear embedding. The paper introduces the method of nonlinear dimension reduction. In this figure, the middle part is the points sampled from the hyper-plane in A. If we flatten the points in B, we could get C. Here we have 3 steps:. 1 For each point, select K neighbors. 2 Find K weights which minimize the squared error between X and WX. 3 In low-dimensional space, find Y (which is corresponding feature of X) which minimize the squared error between Y and WY.

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Nonlinear dimensionality reduction by locally linear embedding. The paper introduces the method of nonlinear dimension reduction. In this figure, the middle part is the points sampled from the hyper-plane in A. If we flatten the points in B, we could get C. Here we have 3 steps:. 1 For each point, select K neighbors. 2 Find K weights which minimize the squared error between X and WX. 3 In low-dimensional space, find Y (which is corresponding feature of X) which minimize the squared error between Y and WY.

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5 william tsao

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7 以電子郵件傳送這篇文章

8 blogthis！

9 分享至 twitter

10 分享至 facebook

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AMMAI | ammaitsao.blogspot.com Reviews

https://ammaitsao.blogspot.com

Nonlinear dimensionality reduction by locally linear embedding. The paper introduces the method of nonlinear dimension reduction. In this figure, the middle part is the points sampled from the hyper-plane in A. If we flatten the points in B, we could get C. Here we have 3 steps:. 1 For each point, select K neighbors. 2 Find K weights which minimize the squared error between X and WX. 3 In low-dimensional space, find Y (which is corresponding feature of X) which minimize the squared error between Y and WY.

INTERNAL PAGES

ammaitsao.blogspot.com

1

AMMAI: Deep neural networks for acoustic modeling in speech recognition

http://ammaitsao.blogspot.com/2016/06/deep-neural-networks-for-acoustic.html

Deep neural networks for acoustic modeling in speech recognition. In speech recognition, GMM-HMM is strong before. But GMM has a big problem. If most points lie on the surface of a sphere, we can use little parameters to model that. But in GMM, it requires very large number of parameters. Now researchers replace GMM with DNN, and they have shown that DNN outperforms GMM. Restricted Boltzmann machine (RBM). 1 First a GRBM is trained to model a window of frames of real-valued acoustic coefficients.

2

AMMAI: 五月 2016

http://ammaitsao.blogspot.com/2016_05_01_archive.html

Deep Compression: Compressing Deep Neural Network with Pruning, Trained Quantization and Huffman Coding. DNN is powerful in computer vision, such as image classifier, detection. But its advantage is that it has too much parameters. If we can discard many parameters but keep the performance, we can save more memory usage. In this paper, we describe 3 stages to remove parameters. Here is the brief summary of this method:. Stage 2: Quantization and weight sharing. Stage 3: Huffman Coding. The main component...

3

AMMAI: 六月 2016

http://ammaitsao.blogspot.com/2016_06_01_archive.html

Nonlinear dimensionality reduction by locally linear embedding. The paper introduces the method of nonlinear dimension reduction. In this figure, the middle part is the points sampled from the hyper-plane in A. If we flatten the points in B, we could get C. Here we have 3 steps:. 1 For each point, select K neighbors. 2 Find K weights which minimize the squared error between X and WX. 3 In low-dimensional space, find Y (which is corresponding feature of X) which minimize the squared error between Y and WY.

4

AMMAI: Faster RCNN

http://ammaitsao.blogspot.com/2016/06/faster-rcnn.html

The work aims to boost the speed and the performance of fast-rcnn. The bottleneck of fast-rcnn is object proposal. So the main method will focus on region proposal. Region Proposal Network (RPN). For RPN, the input is CNN conv feature map, and the output is rectangular object proposals along with object scores. In fast-rcnn, selective search is used, and RPN will replace it in faster-rcnn. Here is the illustration. Result Compared with Fast-rcnn:. 訂閱：張貼留言 (Atom). 簡單主題技術提供： Blogger.

5

AMMAI: 三月 2016

http://ammaitsao.blogspot.com/2016_03_01_archive.html

Iterative Quantization: A Procrustean Approach to Learning Binary Codes. We usually use d-dimension vector to represent an image, but if we consider the usage of memory, we hope to use smaller dimension binary vector to represent an image. But when we do this way, we may have some error, the error is better to be smaller. And the other way, for a new image, the algorithm should be efficient to calculate the binary representation vector. X to be the initial W. Where V=XW, B is the encoding result, and R i...

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ammaitsao.blogspot.com

AMMAI

Nonlinear dimensionality reduction by locally linear embedding. The paper introduces the method of nonlinear dimension reduction. In this figure, the middle part is the points sampled from the hyper-plane in A. If we flatten the points in B, we could get C. Here we have 3 steps:. 1 For each point, select K neighbors. 2 Find K weights which minimize the squared error between X and WX. 3 In low-dimensional space, find Y (which is corresponding feature of X) which minimize the squared error between Y and WY.

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