第十周根本没时间上课，只能利用第11周的春假补全。
This week: going over Feature Transformation this week, and starting on Information Theory.
- Feature selection is a subset of feature transformation
- Transformation operator is linear combinations of original features
Why do Feature Transformation
- XOR, Kernel methods, Neural networks already do FT.
- ad hoc Information Retrieval Problem: finding documents within
a corpus that are relevant to an information need specified using a query. (Query is unknown)
- Problems of Information Retrieval:
- Polysemy: e.g. a word have multiple meanings; cause false positive problem
- Synonymy: e.g. a meaning can be expressed by multiple words. can cause false negatives problems.
PCA
This paper does a fantastic job building the intuition and implementation behind PCA
An eigenproblem is a computational problem that can be solved by finding the eigenvalues and/or eigenvectors of a matrix. In PCA, we are analyzing the covariance matrix (see the paper for details)
PCA Features
- maximize variance
- mutually orthogonal (every components are perpendicular to each other)
- Global algorithm: the resulted components have a global constraint which is that they must be orthogonal
it gives best reconstruction
EigenValue monotonically not increasing and 0 eigenvalue = ignorable (irrelevant, maybe not useful).
It's well studied and fast to run.
- it's like a classification. and using a filtering method to select dimensions to use.
- PCA is about finding
ICA
ICA has also been applied to the information retrieval problem, in a paper written by Charles himself
- find components that are statistically independent from each other using mutual information.
- Designed to solve the blind source separation problem.
- Model: given observables, find hidden variables.
- ICA is more suitable for BSS problems and is directional.
- Eg,
- PCA on faces will separate image based on brightness and average faces. ICA will get features such as nose, mouth etc, which are basic components of a face.
Alternatives:
Random components Analysis: generates random directions
- Can project to smaller dimensions (m << n)but in practice often have more dimensions than PCA.
- Can project to higher dimensions (m > n)
- It works and works very fast.
- Linear Discriminant analysis: find a projection that discriminates based on the label
wrap up
This excellent paper is a great resource for the Feature Transformation methods from this course, and beyond
2016-03-17 初稿
2016-03-26 补完