  In this paper, we investigate the problem of learning feature representation
from unlabeled data using a single-layer K-means network. A K-means network
maps the input data into a feature representation by finding the nearest
centroid for each input point, which has attracted researchers' great attention
recently due to its simplicity, effectiveness, and scalability. However, one
drawback of this feature mapping is that it tends to be unreliable when the
training data contains noise. To address this issue, we propose a SVDD based
feature learning algorithm that describes the density and distribution of each
cluster from K-means with an SVDD ball for more robust feature representation.
For this purpose, we present a new SVDD algorithm called C-SVDD that centers
the SVDD ball towards the mode of local density of each cluster, and we show
that the objective of C-SVDD can be solved very efficiently as a linear
programming problem. Additionally, traditional unsupervised feature learning
methods usually take an average or sum of local representations to obtain
global representation which ignore spatial relationship among them. To use
spatial information we propose a global representation with a variant of SIFT
descriptor. The architecture is also extended with multiple receptive field
scales and multiple pooling sizes. Extensive experiments on several popular
object recognition benchmarks, such as STL-10, MINST, Holiday and Copydays
shows that the proposed C-SVDDNet method yields comparable or better
performance than that of the previous state of the art methods.
