Good places to start:
Optimal feature extractors, that's better than PCA because you whiten your
inter class scatter and so put all inter class comparisons on the same
level. The good thing is this will also reduce your feature vector
dimensionality to c-1 (where c is # classes). PCA will not do
Corection typo: Should read 'Whiten intra class scatter'
Mark Harrison [EMAIL PROTECTED] wrote in message
news:FIif8.16518$[EMAIL PROTECTED];
Good places to start:
Optimal feature extractors, that's better than PCA because you whiten your
inter class scatter and so put all inter class
Hi all,
I recieved numerous replies to my query. I can't thanks everyone
individually so I want to thank everyone who has replied. I am now looking
through the information and links that you have provided.
Many Thanks For All Your Help!!
Rishabh
Rishabh Gupta [EMAIL PROTECTED] wrote in
Rishabh Gupta [EMAIL PROTECTED] wrote in message
a4eje9$ip8$[EMAIL PROTECTED]">news:a4eje9$ip8$[EMAIL PROTECTED]...
Hi All,
I'm a research student at the Department Of Electronics, University Of
York, UK. I'm working a project related to music analysis and
classification.
Hi All,
I'm a research student at the Department Of Electronics, University Of
York, UK. I'm working a project related to music analysis and
classification. I am at the stage where I perform some analysis on music
files (currently only in MIDI format) and extract about 500 variables that
are
Rishabh Gupta [EMAIL PROTECTED] wrote in
a4eje9$ip8$[EMAIL PROTECTED]:">news:a4eje9$ip8$[EMAIL PROTECTED]:
Hi All,
I'm a research student at the Department Of Electronics, University
Of
York, UK. I'm working a project related to music analysis and
classification. I am at the stage
In sci.stat.math Rishabh Gupta [EMAIL PROTECTED] wrote:
[ snip ]
It seems that you are new to the field of pattern recognition.
In that case, you may want to check out the classic book
Pattern Classification by Duda, Hart and Stork.
There is a second edition that came out in 2001. It is a
classification is a specialized field go to
http://www.pitt.edu/~csna/
and click on class-l
although this is the Classification Society of North America members of the
British Classification Society also follow it.
SPSS should be able to handle what you want to do. However, you need
Rishabh Gupta [EMAIL PROTECTED] wrote in message
a4eje9$ip8$[EMAIL PROTECTED]">news:a4eje9$ip8$[EMAIL PROTECTED]...
Hi All,
I'm a research student at the Department Of Electronics, University Of
York, UK. I'm working a project related to music analysis and
classification. I am at the
Genres are presumably groups. So linear combinations of variables that
best separate the genres would be more effectively found by linear
canonical variates analysis (aka discriminant analysis).
Richard Wright
On Thu, 14 Feb 2002 03:18:48 GMT, Jim Snow [EMAIL PROTECTED]
wrote:
snipped
You might consider a form of PLS - your measurmenets may be highly correlated,
and only a very few can do you any good. You have a great many output vars,
and few enough inputs.
Jay
Rishabh Gupta wrote:
Hi All,
I'm a research student at the Department Of Electronics, University Of
Richard Wright [EMAIL PROTECTED] wrote in message
[EMAIL PROTECTED]">news:[EMAIL PROTECTED]...
Genres are presumably groups. So linear combinations of variables that
best separate the genres would be more effectively found by linear
canonical variates analysis (aka discriminant analysis).
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