nearest-neighbor | 易学教程

How to understand Locality Sensitive Hashing?

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I noticed that LSH seems a good way to find similar items with high-dimension properties. After reading the paper http://www.slaney.org/malcolm/yahoo/Slaney2008-LSHTutorial.pdf , I'm still confused with those formulas. Does anyone know a blog or article that explains that the easy way? The best tutorial I have seen for LSH is in the book: Mining of Massive Datasets. Check Chapter 3 - Finding Similar Items http://infolab.stanford.edu/~ullman/mmds/ch3a.pdf Also I recommend the below slide: http://www.cs.jhu.edu/%7Evandurme/papers/VanDurmeLallACL10-slides.pdf . The example in the slide helps me a

Nearest neighbors in high-dimensional data?

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I have asked a question a few days back on how to find the nearest neighbors for a given vector. My vector is now 21 dimensions and before I proceed further, because I am not from the domain of Machine Learning nor Math, I am beginning to ask myself some fundamental questions: Is Euclidean distance a good metric for finding the nearest neighbors in the first place? If not, what are my options? In addition, how does one go about deciding the right threshold for determining the k-neighbors? Is there some analysis that can be done to figure this value out? Previously, I was suggested to use kd

Nearest-neighbor interpolation algorithm in MATLAB

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问题 I am trying to write my own function for scaling up an input image by using the Nearest-neighbor interpolation algorithm. The bad part is I am able to see how it works but cannot find the algorithm itself. I will be grateful for any help. Here\'s what I tried for scaling up the input image by a factor of 2: function output = nearest(input) [x,y]=size(input); output = repmat(uint8(0),x*2,y*2); [newwidth,newheight]=size(output); for i=1:y for j=1:x xloc = round ((j * (newwidth+1)) / (x+1));

How to understand Locality Sensitive Hashing?

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问题 I noticed that LSH seems a good way to find similar items with high-dimension properties. After reading the paper http://www.slaney.org/malcolm/yahoo/Slaney2008-LSHTutorial.pdf, I\'m still confused with those formulas. Does anyone know a blog or article that explains that the easy way? 回答1: The best tutorial I have seen for LSH is in the book: Mining of Massive Datasets. Check Chapter 3 - Finding Similar Items http://infolab.stanford.edu/~ullman/mmds/ch3a.pdf Also I recommend the below slide:

Nearest neighbors in high-dimensional data?

阅读更多关于 Nearest neighbors in high-dimensional data?

问题 I have asked a question a few days back on how to find the nearest neighbors for a given vector. My vector is now 21 dimensions and before I proceed further, because I am not from the domain of Machine Learning nor Math, I am beginning to ask myself some fundamental questions: Is Euclidean distance a good metric for finding the nearest neighbors in the first place? If not, what are my options? In addition, how does one go about deciding the right threshold for determining the k-neighbors? Is