Slow Feature Analysis: Unsupervised Learning of Invariances
نویسندگان
چکیده
منابع مشابه
Slow Feature Analysis: Unsupervised Learning of Invariances
Invariant features of temporally varying signals are useful for analysis and classification. Slow feature analysis (SFA) is a new method for learning invariant or slowly varying features from a vectorial input signal. It is based on a nonlinear expansion of the input signal and application of principal component analysis to this expanded signal and its time derivative. It is guaranteed to find ...
متن کاملUnsupervised discovery of invariances
The grey level profiles of adjacent image regions tend to be different, whilst the ‘hidden’ physical parameters associated with these regions (e.g. surface depth, edge orientation) tend to have similar values. We demonstrate that a network in which adjacent units receive inputs from adjacent image regions learns to code for hidden parameters. The learning rule takes advantage of the spatial smo...
متن کاملUnsupervised Feature Learning for Audio Analysis
Identifying acoustic events from a continuously streaming audio source is of interest for many applications including environmental monitoring for basic research. In this scenario neither different event classes are known nor what distinguishes one class from another. Therefore, an unsupervised feature learning method for exploration of audio data is presented in this paper. It incorporates the...
متن کاملFeature Selection for Unsupervised Learning
In this paper, we identify two issues involved in developing an automated feature subset selection algorithm for unlabeled data: the need for finding the number of clusters in conjunction with feature selection, and the need for normalizing the bias of feature selection criteria with respect to dimension. We explore the feature selection problem and these issues through FSSEM (Feature Subset Se...
متن کاملAnalysis of Unsupervised Feature Learning in Image Segmentation
Unsupervised feature learning was proved to be a potentially powerful tool for image segmentation as pixel-wise classification. However, there is no comprehensive study on the importance of each module of image segmentation pipeline. In this project we aim to understand the formulated variability of performance of feature learning methods in the context of image segmentation. A generic test fra...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Neural Computation
سال: 2002
ISSN: 0899-7667,1530-888X
DOI: 10.1162/089976602317318938