A Poor Man's Parallel Processing

A very crude, but often good enough, method to achieve parallel processing (e.g., on multi-core computers) is to partition the large input data file into small chunks, run the program to process each of them in parallel, and then merge the output results file back. Fortunately, this process can be done easily with the wise iterative usage of two Unix utilities: split and cat.

nDCG

The choice of the gain and discount function for the popular IR performance measure normalised Discounted Cumulative Gain (nDCG) has been discussed and empirically justified in a CIKM-2009 paper through analysis of variance (ANOVA).

LNRE

Here is a good tutorial with Matlab examples about Statistical Estimation for Large Numbers of Rare Events (LNRE).

VLFeat - a computer vision toolbox

The VLFeat open source computer vision library that implements popular

• feature extraction algorithms (such as SIFT, MSER, and quick shift),
  
• clustering algorithms (such as integer k-means, hierarchical k-means, and agglomerative information bottleneck), and
  
• matching algorithms (such as randomized kd-trees).
  

It is written in C for efficiency and compatibility, with interfaces in MATLAB for ease of use, and detailed documentation throughout.

Bloom filters and Locality Sensitive Hashing

Locality Sensitive Hashing (LSH) of l-bits is achieved by carrying out l independent random cuts of the Euclidean space: if two data points are in the same side of all these cuts, they are very likely to be nearest neighbours. In this sense, I think Bloom filters (that also relies on a number of independent hashing functions) can be conceptually considered as the extreme case of LSH: each of its cuts tries to separate one data point from the rest.