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Publikováno v:
IJCNN
Multiple-kernel k-means (MKKM) clustering has demonstrated good clustering performance by combining pre-specified kernels. In this paper, we argue that deep relationships within data and the complementary information among them can improve the perfor
Publikováno v:
IJCNN
For big, high-dimensional dense features, it is important to learn compact binary codes or compress them for greater memory efficiency. This paper proposes a Binarized Multilinear PCA (BMP) method for this problem with Free-Form Reshaping (FFR) of su
Autor:
Shayan Garani Srinivasa, Prayag Gowgi
Publikováno v:
IJCNN
We look at the neural network as a non-linear probability density function (pdf) transformer by stochastic learning cumulative (SLC) technique. We formulate a potential function that drives a neural network to non-linearly transform the input pdf to
Publikováno v:
IJCNN
In this paper, we introduce a data-dependent kernel called deep quasi-linear kernel, which can directly gain a profit from a pre-trained feedforward deep network. Firstly, a multi-layer gated bilinear classifier is formulated to mimic the functionali
Publikováno v:
IJCNN
The importance of the q-Gaussian distributions is attributed to their power law nature and the fact that they generalize the Gaussian distributions (q → 1 retrieves the Gaussian distributions). While for q > 1, a q-Gaussian distribution is nothing
Publikováno v:
IJCNN
In order to reduce the computational complexity of kernel machines and improve their performance in multi-label classification, we develop a systematic two step batch approach for constructing and training a new multiclass kernel machine (MKM). The p
Publikováno v:
IJCNN
In this paper we introduce a new learning approach, which provides automated topological co-clustering based on Self-Organizing Map. The proposed approach (wd-TCoC) is computationally simple, learns a different feature's weights vector for each proto
Autor:
Inanc Birol, Djallel Bouneffouf
Publikováno v:
IJCNN
Spectral clustering is a powerful approach for clustering, with applications across multiple disciplines, including bioinformatics. However, the way its computational complexity scales limits its application in analyzing large datasets. This complexi
Publikováno v:
IJCNN
We introduce Sparse Entropy Clustering (SEC) which uses minimum entropy criterion to split high dimensional binary vectors into groups. The idea is based on the analogy between clustering and data compression: every group is reflected by a single enc
Publikováno v:
IJCNN
Kernel Spectral Clustering (KSC) solves a weighted kernel principal component analysis problem in a primal-dual optimization framework. It builds an unsupervised model on a small subset of data using the dual solution of the optimization problem. Thi