It is more than a century since Karl Pearson invented the concept of Principal
Component Analysis (PCA). Nowadays, it is a very useful tool in data analysis in
many fields. PCA is the technique of dimensionality reduction, which transforms
data in the high-dimensional space to space of lower dimensions. The advantages of
this subspace are numerous. First of all, the reduced dimension has the effect of
retaining the most of the useful information while reducing noise and other
undesirable artifacts. Secondly, the time and memory that used in data processing
The Self-Organizing Map (SOM) is a neural network algorithm, which uses a competitive learning technique to train itself in an unsupervised manner. SOMs are different from other artificial neural networks in the sense that they use a neighborhood function to preserve the topological properties of the input space and they have been used to create an ordered representation of multi-dimensional data which simplifies complexity and reveals meaningful relationships. Prof. T. Kohonen in the early 1980s first established the relevant theory and explored possible applications of SOMs. ...
The real estate impact of arts and cultural activities
is seen not only in the redevelopment of discrete
buildings, but in the incremental renewal of large
districts involving complex social and design solutions.
The physical expression of place-making by the
creative sector often plays out over decades. Older
urban neighborhoods are filled with architecturally-
distinct buildings that exist in the interplay of recently
re-built and longer term deteriorated sites.