NDLI: An Online Clustering Algorithm That Ignores Outliers: Application to Hierarchical Feature Learning from Sensory Data

Content Provider	IEEE Xplore Digital Library
Author	Banerjee, B. Dutta, J.K.
Copyright Year	2013
Description	Author affiliation: Dept. of Electr. & Comput. Eng., Univ. of Memphis, Memphis, TN, USA (Banerjee, B.; Dutta, J.K.)
Abstract	Surveillance sensors are a major source of unstructured Big Data. Discovering and recognizing spatiotemporal objects (e.g., events) in such data is of paramount importance to the security and safety of facilities and individuals. Hierarchical feature learning is at the crux to the problems of discovery and recognition. We present a multilayered convergent neural architecture for storing repeating spatially and temporally coincident patterns in data at multiple levels of abstraction. The bottom-up weights in each layer are learned to encode a hierarchy of over complete and sparse feature dictionaries from space- and time-varying sensory data by recursive layer-by-layer spherical clustering. This density-based clustering algorithm ignores outliers by the use of a unique adaptive threshold in each neuron's transfer function. The model scales to full-sized high-dimensional input data and also to an arbitrary number of layers, thereby possessing the capability to capture features at any level of abstraction. It is fully-learnable with only two manually tunable parameters. The model was deployed to learn meaningful feature hierarchies from audio, images and videos which can then be used for recognition and reconstruction. Besides being online, operations in each layer of the model can be implemented in parallelized hardware, making it very efficient for real world Big Data applications.
Sponsorship	Toshiba
Starting Page	505
Ending Page	512
File Size	609946
Page Count	8
File Format	PDF
e-ISBN	9781479931422
DOI	10.1109/ICDMW.2013.135
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-07
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Radio frequency Neurons Hebbian rule spherical clustering Clustering algorithms Computer architecture outlier Data models Feedforward neural networks Videos
Content Type	Text
Resource Type	Article

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