我如何将Android模拟器连接到本地主机服务器？(How do I connect an Android Emulator to localhost server?)

 我目前正在研究这样的问题 - 分类，最近邻搜索 - 音乐信息检索。 
 
 您可能对近似近邻 （ ANN ）算法感兴趣。 这个想法是允许算法在邻近位置附近返回（可能不是最近的邻居）; 在这样做时，您可以降低复杂性。 你提到了kd-tree ; 这就是一个例子。 但正如你所说， kd-tree在高维度方面表现不佳。 事实上，目前所有的索引技术（基于空间划分）都会降到线性搜索足够高的维度[1] [2] [3]。 
 
 在最近提出的ANN算法中，也许最受欢迎的是局部敏感哈希 （ LSH ），它将高维空间中的一组点映射成一组分组，即哈希表[1] [3]。 但是，与传统散列不同， 地理位置敏感的散列将附近的点放在同一个bin中。 
 
 LSH有一些巨大的优势。 首先，很简单 您只需计算数据库中所有点的哈希值，然后从中创建哈希表。 要查询，只需计算查询点的哈希值，然后从哈希表中检索同一个bin中的所有点。 
 
 第二，有一个严格的理论来支持它的表现。 可以看出，查询时间在数据库的大小上是线性的，即比线性搜索更快。 更快的速度取决于我们可以忍受多少近似值。 
 
 最后， LSH与0 < p <= 2任何Lp范数兼容。 因此，为了回答您的第一个问题，您可以使用LSH与欧氏距离度量，或者您可以使用曼哈顿（L1）距离度量。 还有汉明距离和余弦相似度的变体。 
 
 2008年，Malcolm Slaney和Michael Casey为IEEE信号处理杂志撰写了一个体面的概述[4]。 
 
 LSH已被应用到任何地方。 你可能想试试看。 
 
 
 [1] Datar，Indyk，Immorlica，Mirrokni，“Locality-Sensitive Hashing Scheme Based on p-Stable Distributions”，2004年。 
 
 [2] Weber，Schek，Blott，“A quantitative analysis and performance studies for similarity-search methods in high-dimensional spaces”，1998年。 
 
 [3] Gionis，Indyk，Motwani，“通过散列的高维度相似性搜索”，1999年。 
 
 [4] Slaney，Casey，“找到最近邻居的地点敏感哈希”，2008年。 

I currently study such problems -- classification, nearest neighbor searching -- for music information retrieval.
 
You may be interested in Approximate Nearest Neighbor (ANN) algorithms. The idea is that you allow the algorithm to return sufficiently near neighbors (perhaps not the nearest neighbor); in doing so, you reduce complexity. You mentioned the kd-tree; that is one example. But as you said, kd-tree works poorly in high dimensions. In fact, all current indexing techniques (based on space partitioning) degrade to linear search for sufficiently high dimensions [1][2][3].
 
Among ANN algorithms proposed recently, perhaps the most popular is Locality-Sensitive Hashing (LSH), which maps a set of points in a high-dimensional space into a set of bins, i.e., a hash table [1][3]. But unlike traditional hashes, a locality-sensitive hash places nearby points into the same bin.
 
LSH has some huge advantages. First, it is simple. You just compute the hash for all points in your database, then make a hash table from them. To query, just compute the hash of the query point, then retrieve all points in the same bin from the hash table.
 
Second, there is a rigorous theory that supports its performance. It can be shown that the query time is sublinear in the size of the database, i.e., faster than linear search. How much faster depends upon how much approximation we can tolerate.
 
Finally, LSH is compatible with any Lp norm for 0 < p <= 2. Therefore, to answer your first question, you can use LSH with the Euclidean distance metric, or you can use it with the Manhattan (L1) distance metric. There are also variants for Hamming distance and cosine similarity.
 
A decent overview was written by Malcolm Slaney and Michael Casey for IEEE Signal Processing Magazine in 2008 [4].
 
LSH has been applied seemingly everywhere. You may want to give it a try.
 
 
[1] Datar, Indyk, Immorlica, Mirrokni, "Locality-Sensitive Hashing Scheme Based on p-Stable Distributions," 2004.
 
[2] Weber, Schek, Blott, "A quantitative analysis and performance study for similarity-search methods in high-dimensional spaces," 1998.
 
[3] Gionis, Indyk, Motwani, "Similarity search in high dimensions via hashing," 1999.
 
[4] Slaney, Casey, "Locality-sensitive hashing for finding nearest neighbors", 2008.