V. CONCLUSION In this correspondence, we proposed a novel DCT-based algorithm for the fast computation of the MCLT. The method is based on two DCTs, two stages of butterfly operations. We also gave...
Near-Maximum Entropy Models for Binary Neural Representations of Natural Images (2008)
Bethge, M., Berens, P., Platt, J. C., Koller, D., Singer, Y., Roweis, S.
Maximum entropy analysis of binary variables provides an elegant way for studying the role of pairwise correlations in neural populations. Unfortunately, these approaches suffer from their poor...
Kernel Measures of Conditional Dependence (2008)
Fukumizu, K., Gretton, A., Sun, X., Schölkopf, B., Platt, J. C., Koller, D., ...
We propose a new measure of conditional dependence of random variables, based on normalized cross-covariance operators on reproducing kernel Hilbert spaces. Unlike previous kernel dependence...
Bayesian Inference for Spiking Neuron Models with a Sparsity Prior (2008)
Gerwinn, S., Macke, J., Seeger, M., Bethge, M., Platt, J. C., Koller, D., ...
Generalized linear models are the most commonly used tools to describe the stimulus selectivity of sensory neurons. Here we present a Bayesian treatment of such models. Using the expectation...
A Kernel Statistical Test of Independence (2008)
Gretton, A., Fukumizu, K., Teo, C.H., Song, L., Schölkopf, B., Smola, A.J., ...
Whereas kernel measures of independence have been widely applied in machine learning (notably in kernel ICA), there is as yet no method to determine whether they have detected statistically...
Receptive Fields without Spike-Triggering (2008)
Macke, J.H., Zeck, G., Bethge, M., Platt, J. C., Koller, D., Singer, Y., ...
Stimulus selectivity of sensory neurons is often characterized by estimating their receptive field properties such as orientation selectivity. Receptive fields are usually derived from the mean (or...
An Analysis of Inference with the Universum (2008)
Sinz, F.H., Chapelle, O., Agarwal, A., Schölkopf, B., Platt, J. C., Koller, D., ...
We study a pattern classification algorithm which has recently been proposed by Vapnik and coworkers. It builds on a new inductive principle which assumes that in addition to positive and negative...
Colored Maximum Variance Unfolding (2008)
Song, L., Smola, A.J., Borgwardt, K., Gretton, A., Platt, J. C., Koller, D., ...
Maximum variance unfolding (MVU) is an effective heuristic for dimensionality reduction. It produces a low-dimensional representation of the data by maximizing the variance of their embeddings while...
Consistent Minimization of Clustering Objective Functions (2008)
Von Luxburg, U., Bubeck, S., Jegelka, S., Kaufmann, M., Platt, J. C., Koller, D., ...
Clustering is often formulated as a discrete optimization problem. The objective is to find, among all partitions of the data set, the best one according to some quality measure. However, in the...
Learning with Transformation Invariant Kernels (2008)
Walder, C., Chapelle, O., Platt, J. C., Koller, D., Singer, Y., Roweis, S.
This paper considers kernels invariant to translation, rotation and dilation. We show that no non-trivial positive definite (p.d.) kernels exist which are radial and dilation invariant, only...
Discriminative K-means for Clustering (2008)
Ye, J., Zhao, Z., Wu, M., Platt, J. C., Koller, D., Singer, Y., ...
We present a theoretical study on the discriminative clustering framework, recently proposed for simultaneous subspace selection via linear discriminant analysis (LDA) and clustering. Empirical...
Estimating the Support of a High-Dimensional Distribution (2001)
Schölkopf, B., Platt, J.C., Shawe-Taylor, J.S., Smola, A.J., Williamson, R.C.
Suppose you are given some data set drawn from an underlying probability distribution P and you want to estimate a “simple” subset S of input space such that the probability that a test point...
Estimating the Support of a High-Dimensional Distribution (2001)
Schölkopf, B., Platt, J.C., Shawe-Taylor, J.S., Smola, A.J., Williamson, R.C.
Suppose you are given some data set drawn from an underlying probability distribution P and you want to estimate a “simple” subset S of input space such that the probability that a test point...
Estimating the Support of a High-Dimensional Distribution (2001)
Schölkopf, B., Platt, J.C., Shawe-Taylor, J.S., Smola, A.J., Williamson, R.C.
Suppose you are given some data set drawn from an underlying probability distribution P and you want to estimate a “simple” subset S of input space such that the probability that a test point...
Estimating the Support of a High-Dimensional Distribution (2001)
Sch"olkopf, B., Platt, J.C., Shawe-Taylor, J.S., Smola, A.J., Williamson, R.C.