Patent Number:
7,788,191
Title:
Ordered data compression system and methods using principle component analysis
Abstract:
Methods and systems are provided for encoding, transmission and decoding of vectorized input data, for example, video or audio data. A convex invariance learning framework is established for processing input data or a given data type. Each input vector is associated with a variable transformation matrix that acts on the vector to invariantly permute the vector elements. Joint invariance and model learning is performed on a training set of invariantly transformed vectors over a constrained space of transformation matrices using maximum likelihood analysis. The maximum likelihood analysis reduces the data volume to a linear subspace volume in which the training data can be modeled by a reduced number of variables. Principal component analysis is used to identify a set of N eigenvectors that span the linear subspace. The set of N eigenvectors is used a basis set to encode input data and to decode compressed data.
Inventors:
Jebara; Tony (New York, NY)
Assignee:
The Trustees of Columbia University in the City of New York
International Classification:
G06F 15/18 (20060101)
Expiration Date:
8/31/12018