I developed an algorithm for using local ICA in denoising multidimensional data. It uses delay embedded version of the data, CLUSTERING and ICA for the separation between data and noise.
An Efficient and Effective Detailed Placement Algorithm
Global Swap
To identify a pair of cells that can be swapped to reduce wirelength (others are fixed).
2. Vertical Swap
Swap a cell with a nearby cell in the segment above or below.
3. Local Re-ordering
Re-order consecutive cells locally to reduce wirelength.
4. Single-Segment CLUSTERING
Place cells optimally within a segment.
Recent advances in experimental methods have resulted in the generation
of enormous volumes of data across the life sciences. Hence CLUSTERING and
classification techniques that were once predominantly the domain of ecologists
are now being used more widely. This book provides an overview of these
important data analysis methods, from long-established statistical methods
to more recent machine learning techniques. It aims to provide a framework
that will enable the reader to recognise the assumptions and constraints that
are implicit in all such techniques. Important generic issues are discussed first
and then the major families of algorithms are described. Throughout the focus
is on explanation and understanding and readers are directed to other resources
that provide additional mathematical rigour when it is required. Examples
taken from across the whole of biology, including bioinformatics, are provided
throughout the book to illustrate the key concepts and each technique’s
potential.
Many of the pattern fi nding algorithms such as decision tree, classifi cation rules and CLUSTERING
techniques that are frequently used in data mining have been developed in machine learning
research community. Frequent pattern and association rule mining is one of the few excep-
tions to this tradition. The introduction of this technique boosted data mining research and its
impact is tremendous. The algorithm is quite simple and easy to implement. Experimenting
with Apriori-like algorithm is the fi rst thing that data miners try to do.
function [U,V,num_it]=fcm(U0,X)
％ MATLAB (Version 4.1) Source Code (Routine fcm was written by Richard J.
％ Hathaway on June 21, 1994.) The fuzzification constant
％ m = 2, and the stopping criterion for successive partitions is epsilon =??????.
％*******Modified 9/15/04 to have epsilon = 0.00001 and fix univariate bug********
％ Purpose:The function fcm attempts to find a useful CLUSTERING of the
％ objects represented by the object data in X using the initial partition in U0.
state of art language modeling methods:
An Empirical Study of Smoothing Techniques for Language Modeling.pdf
BLEU, a Method for Automatic Evaluation of Machine Translation.pdf
Class-based n-gram models of natural language.pdf
Distributed Language Modeling for N-best List Re-ranking.pdf
Distributed Word CLUSTERING for Large Scale Class-Based Language Modeling in.pdf
Quartz is a full-featured, open source job scheduling system that can be integrated with, or used along side virtually any J2EE or J2SE application - from the smallest stand-alone application to the largest e-commerce system. Quartz can be used to create simple or complex schedules for executing tens, hundreds, or even tens-of-thousands of jobs jobs whose tasks are defined as standard Java components or EJBs. The Quartz Scheduler includes many enterprise-class features, such as JTA transactions and CLUSTERING.
統計模式識別工具箱（Statistical Pattern Recognition Toolbox）包含：
1，Analysis of linear discriminant function
2，Feature extraction: Linear Discriminant Analysis
3，Probability distribution estimation and CLUSTERING
4，Support Vector and other Kernel Machines
