Creat membership Creat membership
Sign in

Forgot password?

Confirm
  • Forgot password?
    Sign Up
  • Confirm
    Sign In
Creat membership Creat membership
Sign in

Forgot password?

Confirm
  • Forgot password?
    Sign Up
  • Confirm
    Sign In
Collection
For ¥0.57 per day, unlimited downloads CREATE MEMBERSHIP Download

toTop

If you have any feedback, Please follow the official account to submit feedback.

Turn on your phone and scan

home > search >

Archetypal analysis for machine learning and data mining

Author:
Morten Mørup   mm@imm.dtu.dk [Author Vitae]   Lars Kai Hansen lkh@imm.dtu.dk [Author Vitae]  


Journal:
Neurocomputing


Issue Date:
2012


Abstract(summary):

Archetypal analysis (aa) proposed by Cutler and Breiman (1994) estimates the principal convex hull (pch) of a data set. As such aa favors features that constitute representative ‘corners?of the data, i.e., distinct aspects or archetypes. We currently show that aa enjoys the interpretability of clustering - without being limited to hard assignment and the uniqueness of svd - without being limited to orthogonal representations. In order to do large scale aa, we derive an efficient algorithm based on projected gradient as well as an initialization procedure we denote FurthestSum that is inspired by the FurthestFirst approach widely used for k-means (Hochbaum and Shmoys, 1985 ). We generalize the aa procedure to kernel-aa in order to extract the principal convex hull in potential infinite Hilbert spaces and derive a relaxation of aa when the archetypes cannot be represented as convex combinations of the observed data. We further demonstrate that the aa model is relevant for feature extraction and dimensionality reduction for a large variety of machine learning problems taken from computer vision, neuroimaging, chemistry, text mining and collaborative filtering leading to highly interpretable representations of the dynamics in the data. Matlab code for the derived algorithms is available for download from .


Page:
54-63


VIEW PDF

The preview is over

If you wish to continue, please create your membership or download this.

Create Membership

Similar Literature

Submit Feedback

This function is a member function, members do not limit the number of downloads