論文讀書筆記 寒假

Paper 1：on the difficulty of nearest neighbor search

這篇論文主要介紹的是在最近鄰搜尋中困難度的相關內容，並且提出了一個具體化的估計困難度的方法（relative contrast），下面是本文摘抄

1、  the term difficulty here has two different but related meanings: in the context of NN search problem, difficulty represents meaningfulness. In the context of approximate NN search methods like tree or hashing based indexing methods, difficulty represents
complexity.

2、  Suppose we are given a data set X containing n d-dim points,X={xi,i=1..n}, and a query q where xi, q are samples from an unknown distribution p(x), let D(,) be the distance function for the d-dimensional data.

Lp distance:

Cr captures the notion of difficulty of NN search in X. Smaller the Cr, more difficult the search. If Cr is close to 1, NN search in database X is not very meaningful.

3、

σ' is usually very small for high dimensional data. It is clear that smaller  leads to smaller relative contrast, more difficult nearest neighbour search.

4、

 

5、dataproperties and relative contrast

Data dimensionality:the larger d will lead to smaller relative contrast, making NN search lessmeaningful.

Data sparsity: thesmaller s will lead to larger relative contrast.

Data size: relativecontrast increases monotonically with n.

Data metric norm:relative contrast with respect to p is not so straightforward.

 

Paper 2. Efficient active algorithm for hierarchical clustering

這篇論文提出的是對於層次聚類的一種演算法，更確切的說是一種計算架構。作者通過研究前人的成果，結合常見的聚類演算法提出自己的改進建議。下面是摘抄

1、A-any flat clustering algorithm,which takes as parameters a dataset and a natural number k, indicating the number of clusters to produce. k –denote the number of clusters at any split, it is known and fixed across the hierarchy. n –the number
of objects in a datasets.s –a parameter, influencing the number of measurements used by the algorithm, smaller s implies fewer measurements,increasing s increase the robustness of our method.K-possibly noisy similarity function, model both cases where similarities
are measured directly and where they are computed via some kernel function based on observed object features.

 

 

Restrictions:

2、  active spectral clustering

 

Paper 3 choosing linguistics over vision to describe images

這篇論文介紹的是如何對一幅圖片進行語言描述，顯然這種描述不是人工而是機器自動實現的。文中提出了相比前人而言更好的方法，得到的描述不再是孤立的單詞，而是一個句子。摘抄如下：

1、作者首先給出了一個例子，指出對每一幅輸入的圖片是如何處理的。

(1)givenan unseen image

(2)findK images most similar to it from the training images, and using the phrasesextracted from their descriptions

(3)generatea ranked list of triples which is then used to compose description for the newimage

在上面的第二步中，對每幅圖片的描述是人為添加的，但是這些人為添加的東西一開始都是一些句子，需要將其分解為((attribute1,object1),verb),(verb,prep,(attribute2,object2)),(object1,prep,object2)這些形式，具體例子如下：

 

2、  model for predicting phrase relevance

3、  phrase integration